{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oENJ3x1S-J1x", "outputId": "6e6cd3f3-352e-43b5-ccc1-4823a9c7ff00" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "# Serve per montare nel runtime di Colab una cartella che corrisponde al tuo Google Drive\n", "\n", "from google.colab import drive\n", "drive.mount('/content/drive');" ] }, { "cell_type": "markdown", "metadata": { "id": "vJqFTnXcejOU" }, "source": [ "Installiamo la libreria transformers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xlVjB6wjeGRC", "outputId": "d569d40d-41b4-4aed-d999-aa5297f8fab2" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting transformers\n", " Downloading transformers-4.29.2-py3-none-any.whl (7.1 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m96.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n", "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n", " Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n", "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n", " Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m98.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.4.0)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n", "Installing collected packages: tokenizers, huggingface-hub, transformers\n", "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.29.2\n" ] } ], "source": [ "!pip install transformers" ] }, { "cell_type": "markdown", "metadata": { "id": "Loa3B16qfM7V" }, "source": [ "Importiamo le librerie necessarie:\n", "\n", "\n", "1. **AutoTokenizer**: modulo di tokenizzazione di HuggingFace che seleziona il tokenizer automaticamente in base al modello NLP utilizzato. Nel nostro caso, visto che utilizzeremo CodeBERT, AutoTokenizer caricherà _BertTokenizer_.\n", "Quest'ultimo, divide il testo in token utilizzando l'algoritmo WordPiece, che suddivide le parole in parti più piccole e più comuni (subword), in modo da poter gestire parole sconosciute o meno frequenti durante l'addestramento.\n", "2. **Auto Model**: modulo che seleziona automaticamente il modello di NLP appropriato in base al nome del modello, che passiamo come parametro, che nel nostro caso, sarà un modello pre-addestrato.\n", "3. **train_test_split**: questa funzione, proviene dalla libreria _sklearn_, e permette di dividere un dataset in due parti: _training-set_ e _test-set_. Rispettivamente, una verrà usata per allenare il modello, mentre l'altra verrà usata per valutare la sua capacità di generalizzazione.\n", "4. **pandas**: offre strumenti per l'analisi di dati in forma tabellare, dataframe e manipolazione di essi. Permette operazioni come applicazione di filtri a colonne, aggregazione e merge di dataframe.\n", "5. **numpy**: elaborazione di array numerici multidimensionali, per quest'ultimi, offre operazioni di algebra lineare e operazioni matematiche.\n", "6. **tabulate**: permette di trasformare un array in una tabella per visualizzare graficamente strutture dati.\n", "7. **tqdm**: permette di visualizzare progressbar a cicli di elaborazione iterativi. Potrebbe essere utile in questa fase dello sviluppo del sistema, per monitorare i tempi che richiedono i vari processi.\n", " \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TPsZ223TfYAs" }, "outputs": [], "source": [ "import torch # framework di ML\n", "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler #manipolazione dei dati\n", "from transformers import RobertaTokenizer, RobertaForSequenceClassification\n", "\n", "from sklearn.model_selection import train_test_split # funzione in sklearn per dividere il dataset in train, test, validation\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from tabulate import tabulate\n", "from tqdm import trange\n", "import random # generazione di numeri random\n", "\n", "import warnings\n", "warnings.filterwarnings(action='once')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oGhgL0E6QG5B" }, "outputs": [], "source": [ "file_path = \"/content/drive/Shareddrives/se4ai/ideal-dataset.xlsx - ideal-dataset_1 (1).CSV (9).csv\"; # Da eseguire se hai montato la cartella di gdrive al primo blocco. (Cambiare la path con path del file .csv)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9gjRGnA-QIJ2" }, "outputs": [], "source": [ "file_path = \"ideal-dataset.csv\"; # Da eseguire se hai caricato il dataset a mano nel runtime" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zJZrmEk9qZOd", "outputId": "fbf267f3-a504-4cd9-a39e-06bcaa95010f" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":914: ImportWarning: APICoreClientInfoImportHook.find_spec() not found; falling back to find_module()\n", ":914: ImportWarning: _PyDriveImportHook.find_spec() not found; falling back to find_module()\n", ":914: ImportWarning: _OpenCVImportHook.find_spec() not found; falling back to find_module()\n", ":914: ImportWarning: _BokehImportHook.find_spec() not found; falling back to find_module()\n", ":914: ImportWarning: _AltairImportHook.find_spec() not found; falling back to find_module()\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 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if l == 'mop':\n", " labels.append(0);\n", " elif l == 'aop':\n", " labels.append(1);\n", " elif l == 'clr':\n", " labels.append(2);\n", " elif l == 'nic':\n", " labels.append(3);\n", " #labels.append(0 if l == 'mop' else 1);\n", "\n", "code = list(df['code']);\n", "print(labels);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zaf1bSFfOgiW" }, "outputs": [], "source": [ "# Divide il dataset in train test validation\n", "# Parametri test_size:\n", "# valori di default: train test_size = 0.33 val test_size = 0.3\n", "TRAIN_TEST_SIZE = 0.33;\n", "VAL_TEST_SIZE = 0.3;\n", "# Tendenzialmente dovrebbero essere simili così train = 70%, test = 30%, val = 30% di test;\n", "# Alzando test e val può migliorare accuracy perché ha più esempi per test e validazione\n", "\n", "train_codes, temp_codes, train_labels, temp_labels = train_test_split(code, labels, test_size = TRAIN_TEST_SIZE, shuffle = True, stratify = labels);\n", "test_codes, val_codes, test_labels, val_labels = train_test_split(temp_codes, temp_labels, test_size = VAL_TEST_SIZE, shuffle = True, stratify = temp_labels );\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "tAQ7yLeVqZ0-" }, "source": [ "inizializziamo il tokenizer ed il modello" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 403, "referenced_widgets": [ "d23f6b9544004d6fab23ca726a569f36", "39fb6d883d69478395c01b51e499cfb8", "327a293613fa40adba846633f45b76e4", "c490c9d1f2e04eda8cb6e62e54690c91", "327e2d2f8b3a4b159eb0346a91a0890a", "53b7aa4bf7554c85ab5160e681f9c27f", "44e2f773371d4d5590cec8b4137ef331", "fa6f7ff2208c4330a67aa69782b16721", "bde1776cc66b4953a02cae79e4c7a430", "eb8936ffe4424fb9baf9cb8b4ac0e9a8", "5a8c1ac9688e46db927d1b2435571c08", "e8ddf48d524a435da571fec4f23c60fc", "0e03f4adbbf943268ce872323b8c3e8f", "75edd07f5c6d47fda1f0d5de9751ca2d", "981e3d88ac9948bba479baa08bf2bbf4", "c6c89c4fbc014c9aba5b88f69c2aeac0", "5502cb0102d64dd6966da02f523d61a5", "f9367e718eed4e0596b9b54cc4f9722c", "83c8646b20374983bdb3cf095d3d67cf", "d1f50e09d2724088b7011c8c4fb25dc8", "809294cf46eb40c8a43d588c664c7b1c", "e989b7684c654e5ea3df5ec8047fa41f", "5eb8ba6d049f411481e56324c3e6e81e", "8a7bf9e65614484080374d327b516db9", "75a1a37680724baca6ccc9e886354dc9", "c04d818b74954cf0b216c352cbeff703", "a5b29c0a7e5a42d695e8289b4a79fe7f", "5a2f6e3e7dda49cdbd2614bf8c9509dd", "6f7514672410432789cc5641ca823949", "306c7fbe511b4684b1d3ef3b968d64dd", "24cf23ceae154c80886847216f467ab5", "6125b4b3f354408f9c7506e9e1d1d2cc", "11672ca464084074ba8b1d86f950e352", "b5a843307eee478abddb921a37cfd610", "39d2f3835fc24ad4b327a7b4c2dbd17a", "853a77b1c1f14ff485d16a8488897403", "1fc00b71181b4ed5a33cb3cf88f2540b", "eade8db96c3a46b8b1f97984dd47ef69", "f043cd256cf94526ad2791ae90a891b8", "be015fe7b35148d4b09f7fb66e10a667", "ff9d9fd19680482cb0a94440ae6251d0", "45b13d951ff84866a6c6718b773ebbfd", "8befe501304444549d9ef128a3afe36a", "e3d3c96d1c544e8e87e6cefeb3f88494", "664442579b4948709320f3544f9efb32", "73e873014c9843a9a7b7085f87fe8615", "4ed552f210274d929e6f9e2713c3d4e0", "215d051a87bb42b7828bab3842c15830", "aae250a9d76a44cca1bc69f7ee11988f", "734d8a94a3da4ad096891ea46e4caebc", "c9f9e94abf614fb48cd58a6f7964dc59", "4b1e77e493a14733b8cc776c576a844f", "18309c53b39a43819c43208c7fde31dc", "b8c9033907274858a5c20b6e16466556", "ceb345ba10ff4519a30ddc8e31a92a2b", "13617fcb213e47e2a6903497d181aa12", "bfb10dc484aa4bdba269f9ef232d7aa6", "1e99f1cefa04467fa453f82c608870db", "e94a16760ff44aa6a06e33d4bc9dbf7f", "b9918de7bf5e4219a9509af3ecc65f11", "b34e982c8a6d4f3f830e6848a0e0c6c5", "ff5b08856f994c33aee4b5016a56f039", "0278a8a574004e2294fbdaf86fc65c88", "a2192865efb142d6926fbe9965c35469", "48357a257b0f4a26974fb35bbf80322c", "b746086deaaf46718e7699bd10dcf2b6" ] }, "id": "UaiZiEJ7qorV", "outputId": "62cede52-920e-47e6-b45c-f7be0fe0df44" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":914: ImportWarning: APICoreClientInfoImportHook.find_spec() not found; falling back to find_module()\n", ":914: ImportWarning: _PyDriveImportHook.find_spec() not found; falling back to find_module()\n", ":914: ImportWarning: _OpenCVImportHook.find_spec() not found; falling back to find_module()\n", ":914: ImportWarning: _BokehImportHook.find_spec() not found; falling back to find_module()\n", ":914: ImportWarning: _AltairImportHook.find_spec() not found; falling back to find_module()\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)olve/main/vocab.json: 0%| | 0.00/899k [00:00 with the following fields:\n", " - input_ids: list of token ids\n", " - token_type_ids: list of token type ids\n", " - attention_mask: list of indices (0,1) specifying which tokens should considered by the model (return_attention_mask = True).\n", " '''\n", " return tokenizer.encode_plus(\n", " input_text,\n", " add_special_tokens = True,\n", " max_length = 150,\n", " truncation=True,\n", " padding='max_length',\n", " return_attention_mask = True,\n", " return_tensors = 'pt'\n", " )\n", "\n", "def preprocessing_batch(data_set):\n", " token_id = []\n", " attention_masks = []\n", " for sample in data_set:\n", " encoding_dict = preprocessing(sample, tokenizer)\n", " token_id.append(encoding_dict['input_ids']) \n", " attention_masks.append(encoding_dict['attention_mask'])\n", " token_id = torch.cat(token_id, dim = 0)\n", " attention_masks = torch.cat(attention_masks, dim = 0)\n", " return token_id,attention_masks;\n", "\n", "train_token_id,train_attention_masks = preprocessing_batch(train_codes);\n", "test_token_id,test_attention_masks = preprocessing_batch(test_codes);\n", "val_token_id,val_attention_masks = preprocessing_batch(val_codes);\n", "\n", "def print_rand_sentence_encoding(text, token_id):\n", " '''Displays tokens, token IDs and attention mask of a random text sample'''\n", " index = random.randint(0, len(text) - 1)\n", " tokens = tokenizer.tokenize(tokenizer.decode(token_id[index]))\n", " token_ids = [i.numpy() for i in token_id[index]]\n", " print(tokens);\n", " table = np.array([tokens, token_ids]).T\n", " print(tabulate(table, \n", " headers = ['Tokens', 'Token IDs'],\n", " tablefmt = 'fancy_grid'))\n", "\n", "#For instance selectiong a random instance of the training set \n", "# print_rand_sentence_encoding(test_codes, train_token_id)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "72_txlYlTsBp" }, "outputs": [], "source": [ "train_labels = torch.tensor(train_labels)\n", "test_labels = torch.tensor(test_labels)\n", "val_labels = torch.tensor(val_labels)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Uem77tH5WLuO" }, "outputs": [], "source": [ "batch_size = 16 # > 90 crash GPU;\n", "\n", "train_set = TensorDataset(train_token_id, \n", " train_attention_masks, \n", " train_labels)\n", "\n", "val_set = TensorDataset(val_token_id, \n", " val_attention_masks, \n", " val_labels)\n", "\n", "test_set = TensorDataset(test_token_id, \n", " test_attention_masks, \n", " test_labels)\n", "\n", "train_dataloader = DataLoader(\n", " train_set,\n", " sampler = RandomSampler(train_set),\n", " batch_size = batch_size\n", " )\n", "\n", "validation_dataloader = DataLoader(\n", " val_set,\n", " sampler = SequentialSampler(val_set),\n", " batch_size = batch_size\n", " )\n", "\n", "\n", "test_dataloader = DataLoader(\n", " test_set,\n", " sampler = SequentialSampler(test_set),\n", " batch_size = batch_size\n", " )" ] }, { "cell_type": "markdown", "source": [ "# Metriche di valutazione del modello\n", "Nella sezione successiva verranno create le funzioni per la valutazione delle performance del modello.\n", "Come sappiamo, per poter " ], "metadata": { "id": "0sUdv5RLjb1p" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4rzHzD-pWtxq" }, "outputs": [], "source": [ "'''\n", "Arg:\n", " preds: l'array di predizioni effettuate dal modello\n", " labels: l'oracolo delle predizioni, le effettive classi alle quali appartengono gli esempi che sono stati valutati\n", " lb_type: la classe per la quale vogliamo andare ad effettuare la valutazione\n", "'''\n", "\n", "def b_tp(preds, labels, lb_type):\n", " '''\n", " vado ad effettuare una somma delle predizioni che il modello ha classificato come positive e che sono effettivamente positive\n", " '''\n", " return sum([pred == label and pred == lb_type for pred, label in zip(preds, labels)])\n", "\n", "def b_fp(preds, labels, lb_type):\n", " '''\n", " la predizione è sbagliata ed è considerata come un istanza della classe considerata quando in realtà non lo è\n", " '''\n", " return sum([pred != label and pred == lb_type for pred, label in zip(preds, labels)])\n", "\n", "def b_tn(preds, labels, lb_type):\n", " '''\n", " la predizione è giusta ed è diversa dalla label che stiamo considerando, quindi doveva essere classificata come un qualcosa di diverso\n", " '''\n", " return sum([pred == label and pred != lb_type for pred, label in zip(preds, labels)])\n", "\n", "def b_fn(preds, labels, lb_type):\n", " '''la classificazione è sbagliata ed è diversa dalla label che stiamo considerando, anche quando in realtà avrebbe dovuto essere uguale'''\n", " return sum([pred != label and label != lb_type for pred, label in zip(preds, labels)])\n", "\n", "\n", "'''\n", " metriche per le singole classi\n", "'''\n", "def b_metrics(preds, labels, lb_type):\n", " '''\n", " Returns the following metrics:\n", " - accuracy = (TP + TN) / N\n", " - precision = TP / (TP + FP)\n", " - recall = TP / (TP + FN)\n", " - specificity = TN / (TN + FP)\n", " '''\n", " preds = np.argmax(preds, axis = 1).flatten()\n", " labels = labels.flatten()\n", " tp = b_tp(preds, labels, lb_type)\n", " tn = b_tn(preds, labels, lb_type)\n", " fp = b_fp(preds, labels, lb_type)\n", " fn = b_fn(preds, labels, lb_type)\n", " b_accuracy = (tp + tn) / len(labels)\n", " b_precision = tp / (tp + fp) #if (tp + fp) > 0 else 'nan'\n", " b_recall = tp / (tp + fn) #if (tp + fn) > 0 else 'nan'\n", " b_specificity = tn / (tn + fp) #if (tn + fp) > 0 else 'nan'\n", " return b_accuracy, b_precision, b_recall, b_specificity\n", "\n", "def total_metrics(preds, labels):\n", " '''\n", " Returns the following metrics:\n", " - accuracy = (TP + TN) / N\n", " - precision = TP / (TP + FP)\n", " - recall = TP / (TP + FN)\n", " - specificity = TN / (TN + FP)\n", " '''\n", " preds = np.argmax(preds, axis = 1).flatten()\n", " labels = labels.flatten()\n", " total_tp = b_tp(preds, labels, 0) + b_tp(preds, labels, 1) + b_tp(preds, labels, 2) + b_tp(preds, labels, 3)\n", " total_fp = b_fp(preds, labels, 0) + b_fp(preds, labels, 1) + b_fp(preds, labels, 2) + b_fp(preds, labels, 3)\n", " total_tn = b_tn(preds, labels, 0) + b_tn(preds, labels, 1) + b_tn(preds, labels, 2) + b_tn(preds, labels, 3)\n", " total_fn = b_fp(preds, labels, 0) + b_fp(preds, labels, 1) + b_fp(preds, labels, 2) + b_fp(preds, labels, 3)\n", "\n", " b_total_precision = total_tp / (total_tp + total_fp) #if (tp + fp) > 0 else 'nan'\n", " b_total_recall = total_tp / (total_tp + total_fn) #if (tp + fn) > 0 else 'nan'\n", " b_total_specificity = total_tn / (total_tn + total_fp) #if (tn + fp) > 0 else 'nan'\n", " return b_total_precision, b_total_recall, b_total_specificity\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lvd66AwUW1GV" }, "outputs": [], "source": [ "optimizer = torch.optim.AdamW(model.parameters(), \n", " lr = 5e-5, # LEARNING RATE DELL'ALGORITMO OTTIMIZZATORE (5e-5 = 5*10^-5 = 0.00005)\n", " eps = 1e-08\n", " )\n", "model.cuda(); # Eseguire per impostare il modello in modo da usare la GPU durante training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dXlSKiTQXB8g", "outputId": "3250a2c6-d8c2-440b-fb38-992af9a70b0b" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "\rEpoch: 0%| | 0/8 [00:00:49: RuntimeWarning: invalid value encountered in long_scalars\n", " b_precision = tp / (tp + fp) #if (tp + fp) > 0 else 'nan'\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "\t - Train loss: 1.3506\n", "\t - Validation Accuracy: 0.2500\n", "\t - Validation Precision: 0.2500\n", "\t - Validation Recall: 0.2500\n", "\t - Validation Specificity: 0.5000\n", "\n", "\n", "\t - Train loss: 1.3506\n", "\t - Validation Accuracy: 0.4375\n", "\t - Validation Precision: 0.4375\n", "\t - Validation Recall: 0.4375\n", "\t - Validation Specificity: 0.6667\n", "\n", "\n", "\t - Train loss: 1.3506\n", "\t - Validation Accuracy: 0.4583\n", "\t - Validation Precision: 0.4583\n", "\t - Validation Recall: 0.4583\n", "\t - Validation Specificity: 0.6944\n", "\n", "\n", "\t - Train loss: 1.3506\n", "\t - Validation Accuracy: 0.4531\n", "\t - Validation Precision: 0.4531\n", "\t - Validation Recall: 0.4531\n", "\t - Validation Specificity: 0.6958\n", "\n", "\n", "\t - Train loss: 1.3506\n", "\t - Validation Accuracy: 0.4625\n", "\t - Validation Precision: 0.4625\n", "\t - Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.3506\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 0.5150\n", "\t - Validation Recall: 0.5150\n", "\t - Validation Specificity: 0.7411\n", "\n", "\n", "\t - Train loss: 1.3436\n", "\t - Validation Accuracy: 0.2500\n", "\t - Validation Precision: 0.2500\n", "\t - Validation Recall: 0.2500\n", "\t - Validation Specificity: 0.5000\n", "\n", "\n", "\t - Train loss: 1.3436\n", "\t - Validation Accuracy: 0.4375\n", "\t - Validation Precision: 0.4375\n", "\t - Validation Recall: 0.4375\n", "\t - Validation Specificity: 0.6667\n", "\n", "\n", "\t - Train loss: 1.3436\n", "\t - Validation Accuracy: 0.4583\n", "\t - Validation Precision: 0.4583\n", "\t - Validation Recall: 0.4583\n", "\t - Validation Specificity: 0.6944\n", "\n", "\n", "\t - Train loss: 1.3436\n", "\t - Validation Accuracy: 0.4531\n", "\t - Validation Precision: 0.4531\n", "\t - Validation Recall: 0.4531\n", "\t - Validation Specificity: 0.6958\n", "\n", "\n", "\t - Train loss: 1.3436\n", "\t - Validation Accuracy: 0.4625\n", "\t - Validation Precision: 0.4625\n", "\t - Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.3436\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 0.5150\n", "\t - Validation Recall: 0.5150\n", "\t - Validation Specificity: 0.7411\n", "\n", "\n", "\t - Train loss: 1.3267\n", "\t - Validation Accuracy: 0.2500\n", "\t - Validation Precision: 0.2500\n", "\t - Validation Recall: 0.2500\n", "\t - Validation Specificity: 0.5000\n", "\n", "\n", "\t - Train loss: 1.3267\n", "\t - Validation Accuracy: 0.4375\n", "\t - Validation Precision: 0.4375\n", "\t - Validation Recall: 0.4375\n", "\t - Validation Specificity: 0.6667\n", "\n", "\n", "\t - Train loss: 1.3267\n", "\t - Validation Accuracy: 0.4583\n", "\t - Validation Precision: 0.4583\n", "\t - Validation Recall: 0.4583\n", "\t - Validation Specificity: 0.6944\n", "\n", "\n", "\t - Train loss: 1.3267\n", "\t - Validation Accuracy: 0.4531\n", "\t - Validation Precision: 0.4531\n", "\t - Validation Recall: 0.4531\n", "\t - Validation Specificity: 0.6958\n", "\n", "\n", "\t - Train loss: 1.3267\n", "\t - Validation Accuracy: 0.4625\n", "\t - Validation Precision: 0.4625\n", "\t - Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.3267\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 0.5150\n", "\t - Validation Recall: 0.5150\n", "\t - Validation Specificity: 0.7411\n", "\n", "\n", "\t - Train loss: 1.3812\n", "\t - Validation Accuracy: 0.2500\n", "\t - Validation Precision: 0.2500\n", "\t - Validation Recall: 0.2500\n", "\t - Validation Specificity: 0.5000\n", "\n", "\n", "\t - Train loss: 1.3812\n", "\t - Validation Accuracy: 0.4375\n", "\t - Validation Precision: 0.4375\n", "\t - Validation Recall: 0.4375\n", "\t - Validation Specificity: 0.6667\n", "\n", "\n", "\t - Train loss: 1.3812\n", "\t - Validation Accuracy: 0.4583\n", "\t - Validation Precision: 0.4583\n", "\t - Validation Recall: 0.4583\n", "\t - Validation Specificity: 0.6944\n", "\n", "\n", "\t - Train loss: 1.3812\n", "\t - Validation Accuracy: 0.4531\n", "\t - Validation Precision: 0.4531\n", "\t - Validation Recall: 0.4531\n", "\t - Validation Specificity: 0.6958\n", "\n", "\n", "\t - Train loss: 1.3812\n", "\t - Validation Accuracy: 0.4625\n", "\t - Validation Precision: 0.4625\n", "\t - Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.3812\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 0.5150\n", "\t - Validation Recall: 0.5150\n", "\t - Validation Specificity: 0.7411\n", "\n", "\n", "\t - Train loss: 1.3289\n", "\t - Validation Accuracy: 0.2500\n", "\t - Validation Precision: 0.2500\n", "\t - Validation Recall: 0.2500\n", "\t - Validation Specificity: 0.5000\n", "\n", "\n", "\t - Train loss: 1.3289\n", "\t - Validation Accuracy: 0.4375\n", "\t - Validation Precision: 0.4375\n", "\t - Validation Recall: 0.4375\n", "\t - Validation Specificity: 0.6667\n", "\n", "\n", "\t - Train loss: 1.3289\n", "\t - Validation Accuracy: 0.4583\n", "\t - Validation Precision: 0.4583\n", "\t - Validation Recall: 0.4583\n", "\t - Validation Specificity: 0.6944\n", "\n", "\n", "\t - Train loss: 1.3289\n", "\t - Validation Accuracy: 0.4531\n", "\t - Validation Precision: 0.4531\n", "\t - Validation Recall: 0.4531\n", "\t - Validation Specificity: 0.6958\n", "\n", "\n", "\t - Train loss: 1.3289\n", "\t - Validation Accuracy: 0.4625\n", "\t - Validation Precision: 0.4625\n", "\t - Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.3289\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 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Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.3091\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 0.5150\n", "\t - Validation Recall: 0.5150\n", "\t - Validation Specificity: 0.7411\n", "\n", "\n", "\t - Train loss: 1.2915\n", "\t - Validation Accuracy: 0.2500\n", "\t - Validation Precision: 0.2500\n", "\t - Validation Recall: 0.2500\n", "\t - Validation Specificity: 0.5000\n", "\n", "\n", "\t - Train loss: 1.2915\n", "\t - Validation Accuracy: 0.4375\n", "\t - Validation Precision: 0.4375\n", "\t - Validation Recall: 0.4375\n", "\t - Validation Specificity: 0.6667\n", "\n", "\n", "\t - Train loss: 1.2915\n", "\t - Validation Accuracy: 0.4583\n", "\t - Validation Precision: 0.4583\n", "\t - Validation Recall: 0.4583\n", "\t - Validation Specificity: 0.6944\n", "\n", "\n", "\t - Train loss: 1.2915\n", "\t - Validation Accuracy: 0.4531\n", "\t - Validation Precision: 0.4531\n", "\t - Validation Recall: 0.4531\n", "\t - Validation Specificity: 0.6958\n", "\n", "\n", "\t - Train loss: 1.2915\n", "\t - Validation Accuracy: 0.4625\n", "\t - Validation Precision: 0.4625\n", "\t - Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.2915\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 0.5150\n", "\t - Validation Recall: 0.5150\n", "\t - Validation Specificity: 0.7411\n", "\n", "\n", "\t - Train loss: 1.2794\n", "\t - Validation Accuracy: 0.2500\n", "\t - Validation Precision: 0.2500\n", "\t - Validation Recall: 0.2500\n", "\t - Validation Specificity: 0.5000\n", "\n", "\n", "\t - Train loss: 1.2794\n", "\t - Validation Accuracy: 0.4375\n", "\t - Validation Precision: 0.4375\n", "\t - Validation Recall: 0.4375\n", "\t - Validation Specificity: 0.6667\n", "\n", "\n", "\t - Train loss: 1.2794\n", "\t - Validation Accuracy: 0.4583\n", "\t - Validation Precision: 0.4583\n", "\t - Validation Recall: 0.4583\n", "\t - Validation Specificity: 0.6944\n", "\n", "\n", "\t - Train loss: 1.2794\n", "\t - Validation Accuracy: 0.4531\n", "\t - Validation Precision: 0.4531\n", "\t - Validation Recall: 0.4531\n", "\t - Validation Specificity: 0.6958\n", "\n", "\n", "\t - Train loss: 1.2794\n", "\t - Validation Accuracy: 0.4625\n", "\t - Validation Precision: 0.4625\n", "\t - Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.2794\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 0.5150\n", "\t - Validation Recall: 0.5150\n", "\t - Validation Specificity: 0.7411\n", "\n", "\n", "\t - Train loss: 1.2604\n", "\t - Validation Accuracy: 0.2500\n", "\t - Validation Precision: 0.2500\n", "\t - Validation Recall: 0.2500\n", "\t - Validation Specificity: 0.5000\n", "\n", "\n", "\t - Train loss: 1.2604\n", "\t - Validation Accuracy: 0.4375\n", "\t - Validation Precision: 0.4375\n", "\t - Validation Recall: 0.4375\n", "\t - Validation Specificity: 0.6667\n", "\n", "\n", "\t - Train loss: 1.2604\n", "\t - Validation Accuracy: 0.4583\n", "\t - Validation Precision: 0.4583\n", "\t - Validation Recall: 0.4583\n", "\t - Validation Specificity: 0.6944\n", "\n", "\n", "\t - Train loss: 1.2604\n", "\t - Validation Accuracy: 0.4531\n", "\t - Validation Precision: 0.4531\n", "\t - Validation Recall: 0.4531\n", "\t - Validation Specificity: 0.6958\n", "\n", "\n", "\t - Train loss: 1.2604\n", "\t - Validation Accuracy: 0.4625\n", "\t - Validation Precision: 0.4625\n", "\t - Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.2604\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 0.5150\n", "\t - Validation Recall: 0.5150\n", "\t - Validation Specificity: 0.7411\n", "\n", "\n", "\t - Train loss: 1.2477\n", "\t - Validation Accuracy: 0.2500\n", "\t - Validation Precision: 0.2500\n", "\t - Validation Recall: 0.2500\n", "\t - Validation Specificity: 0.5000\n", "\n", "\n", "\t - Train loss: 1.2477\n", "\t - Validation Accuracy: 0.4375\n", "\t - Validation Precision: 0.4375\n", "\t - Validation Recall: 0.4375\n", "\t - Validation Specificity: 0.6667\n", "\n", "\n", "\t - Train loss: 1.2477\n", "\t - Validation Accuracy: 0.4583\n", "\t - Validation Precision: 0.4583\n", "\t - Validation Recall: 0.4583\n", "\t - Validation Specificity: 0.6944\n", "\n", "\n", "\t - Train loss: 1.2477\n", "\t - Validation Accuracy: 0.4531\n", "\t - Validation Precision: 0.4531\n", "\t - Validation Recall: 0.4531\n", "\t - Validation Specificity: 0.6958\n", "\n", "\n", "\t - Train loss: 1.2477\n", "\t - Validation Accuracy: 0.4625\n", "\t - Validation Precision: 0.4625\n", "\t - Validation Recall: 0.4625\n", "\t - Validation Specificity: 0.7067\n", "\n", "\n", "\t - Train loss: 1.2477\n", "\t - Validation Accuracy: 0.5150\n", "\t - Validation Precision: 0.5150\n", "\t - Validation Recall: 0.5150\n", "\t - Validation Specificity: 0.7411\n", "\n", "\n", "\t - Train loss: 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Validation Precision: 0.7625\n", "\t - Validation Recall: 0.7625\n", "\t - Validation Specificity: 0.9046\n", "\n", "\n", "\t - Train loss: 0.0376\n", "\t - Validation Accuracy: 0.7650\n", "\t - Validation Precision: 0.7650\n", "\t - Validation Recall: 0.7650\n", "\t - Validation Specificity: 0.9060\n", "\n", "\n", "\t - Train loss: 0.0367\n", "\t - Validation Accuracy: 0.7500\n", "\t - Validation Precision: 0.7500\n", "\t - Validation Recall: 0.7500\n", "\t - Validation Specificity: 0.9000\n", "\n", "\n", "\t - Train loss: 0.0367\n", "\t - Validation Accuracy: 0.7500\n", "\t - Validation Precision: 0.7500\n", "\t - Validation Recall: 0.7500\n", "\t - Validation Specificity: 0.9000\n", "\n", "\n", "\t - Train loss: 0.0367\n", "\t - Validation Accuracy: 0.7292\n", "\t - Validation Precision: 0.7292\n", "\t - Validation Recall: 0.7292\n", "\t - Validation Specificity: 0.8895\n", "\n", "\n", "\t - Train loss: 0.0367\n", "\t - Validation Accuracy: 0.7344\n", "\t - Validation Precision: 0.7344\n", "\t - Validation Recall: 0.7344\n", "\t - Validation Specificity: 0.8921\n", "\n", "\n", "\t - Train loss: 0.0367\n", "\t - Validation Accuracy: 0.7625\n", "\t - Validation Precision: 0.7625\n", "\t - Validation Recall: 0.7625\n", "\t - Validation Specificity: 0.9046\n", "\n", "\n", "\t - Train loss: 0.0367\n", "\t - Validation Accuracy: 0.7650\n", "\t - Validation Precision: 0.7650\n", "\t - Validation Recall: 0.7650\n", "\t - Validation Specificity: 0.9060\n", "\n", "\n", "\t - Train loss: 0.0359\n", "\t - Validation Accuracy: 0.7500\n", "\t - Validation Precision: 0.7500\n", "\t - Validation Recall: 0.7500\n", "\t - Validation Specificity: 0.9000\n", "\n", "\n", "\t - Train loss: 0.0359\n", "\t - Validation Accuracy: 0.7500\n", "\t - Validation Precision: 0.7500\n", "\t - Validation Recall: 0.7500\n", "\t - Validation Specificity: 0.9000\n", "\n", "\n", "\t - Train loss: 0.0359\n", "\t - Validation Accuracy: 0.7292\n", "\t - Validation Precision: 0.7292\n", "\t - Validation Recall: 0.7292\n", "\t - Validation Specificity: 0.8895\n", "\n", "\n", "\t - Train loss: 0.0359\n", "\t - Validation Accuracy: 0.7344\n", "\t - Validation Precision: 0.7344\n", "\t - Validation Recall: 0.7344\n", "\t - Validation Specificity: 0.8921\n", "\n", "\n", "\t - Train loss: 0.0359\n", "\t - Validation Accuracy: 0.7625\n", "\t - Validation Precision: 0.7625\n", "\t - Validation Recall: 0.7625\n", "\t - Validation Specificity: 0.9046\n", "\n", "\n", "\t - Train loss: 0.0359\n", "\t - Validation Accuracy: 0.7650\n", "\t - Validation Precision: 0.7650\n", "\t - Validation Recall: 0.7650\n", "\t - Validation Specificity: 0.9060\n", "\n", "\n", "\t - Train loss: 0.0360\n", "\t - Validation Accuracy: 0.7500\n", "\t - Validation Precision: 0.7500\n", "\t - Validation Recall: 0.7500\n", "\t - Validation Specificity: 0.9000\n", "\n", "\n", "\t - Train loss: 0.0360\n", "\t - Validation Accuracy: 0.7500\n", "\t - Validation Precision: 0.7500\n", "\t - Validation Recall: 0.7500\n", "\t - Validation Specificity: 0.9000\n", "\n", "\n", "\t - Train loss: 0.0360\n", "\t - Validation Accuracy: 0.7292\n", "\t - Validation Precision: 0.7292\n", "\t - Validation Recall: 0.7292\n", "\t - Validation Specificity: 0.8895\n", "\n", "\n", "\t - Train loss: 0.0360\n", "\t - Validation Accuracy: 0.7344\n", "\t - Validation Precision: 0.7344\n", "\t - Validation Recall: 0.7344\n", "\t - Validation Specificity: 0.8921\n", "\n", "\n", "\t - Train loss: 0.0360\n", "\t - Validation Accuracy: 0.7625\n", "\t - Validation Precision: 0.7625\n", "\t - Validation Recall: 0.7625\n", "\t - Validation Specificity: 0.9046\n", "\n", "\n", "\t - Train loss: 0.0360\n", "\t - Validation Accuracy: 0.7650\n", "\t - Validation Precision: 0.7650\n", "\t - Validation Recall: 0.7650\n", "\t - Validation Specificity: 0.9060\n", "\n", "\n", "\t - Train loss: 0.0353\n", "\t - Validation Accuracy: 0.7500\n", "\t - Validation Precision: 0.7500\n", "\t - Validation Recall: 0.7500\n", "\t - Validation Specificity: 0.9000\n", "\n", "\n", "\t - Train loss: 0.0353\n", "\t - Validation Accuracy: 0.7500\n", "\t - Validation Precision: 0.7500\n", "\t - Validation Recall: 0.7500\n", "\t - Validation Specificity: 0.9000\n", "\n", "\n", "\t - Train loss: 0.0353\n", "\t - Validation Accuracy: 0.7292\n", "\t - Validation Precision: 0.7292\n", "\t - Validation Recall: 0.7292\n", "\t - Validation Specificity: 0.8895\n", "\n", "\n", "\t - Train loss: 0.0353\n", "\t - Validation Accuracy: 0.7344\n", "\t - Validation Precision: 0.7344\n", "\t - Validation Recall: 0.7344\n", "\t - Validation Specificity: 0.8921\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Epoch: 100%|██████████| 8/8 [05:55<00:00, 44.48s/it]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "\t - Train loss: 0.0353\n", "\t - Validation Accuracy: 0.7625\n", "\t - Validation Precision: 0.7625\n", "\t - Validation Recall: 0.7625\n", "\t - Validation Specificity: 0.9046\n", "\n", "\n", "\t - Train loss: 0.0353\n", "\t - Validation Accuracy: 0.7836\n", "\t - Validation Precision: 0.7836\n", "\t - Validation Recall: 0.7836\n", "\t - Validation Specificity: 0.9138\n", "\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] } ], "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Sceglie GPU come device a cui mandare le tuple\n", "\n", "epochs = 8 # Se alle ultime epoche continua a salire l'accuracy, o in generale non converge ancora molto, provare ad aumentare\n", "# Se invece converge troppo presto vengono esesguite epoche inutili in cui il modello non migliora\n", "\n", "for _ in trange (epochs, desc = 'Epoch'):\n", " model.train()\n", " tr_loss = 0\n", " nb_tr_examples, nb_tr_steps = 0, 0\n", "\n", " for step, batch in enumerate(train_dataloader):\n", " batch = tuple(t.to(device) for t in batch)\n", " b_input_ids, b_input_mask, b_labels = batch\n", " #reset gradient value for the new epoch\n", " optimizer.zero_grad()\n", " # Forward pass\n", " train_output = model(b_input_ids, \n", " token_type_ids = None, \n", " attention_mask = b_input_mask, \n", " labels = b_labels)\n", " # Backward pass\n", " train_output.loss.backward()\n", " optimizer.step()\n", " # Update tracking variables\n", " tr_loss += train_output.loss.item()\n", " nb_tr_examples += b_input_ids.size(0)\n", " nb_tr_steps += 1\n", "\n", " # ========== Validation ==========\n", "\n", " # Set model to evaluation mode\n", " model.eval()\n", "\n", " # Tracking variables \n", " val_accuracy = []\n", " val_precision = []\n", " val_recall = []\n", " val_specificity = []\n", "\n", " latest_acc = 0.0000;\n", "\n", " for batch in validation_dataloader:\n", " batch = tuple(t.to(device) for t in batch)\n", " b_input_ids, b_input_mask, b_labels = batch\n", " with torch.no_grad():\n", " # Forward pass\n", " eval_output = model(b_input_ids, \n", " token_type_ids = None, \n", " attention_mask = b_input_mask)\n", " logits = eval_output.logits.detach().cpu().numpy()\n", " label_ids = b_labels.to('cpu').numpy()\n", " # Calculate validation metrics for each class\n", " b_accuracy_mop, b_precision_mop, b_recall_mop, b_specificity_mop = b_metrics(logits, label_ids, 0)\n", " b_accuracy_aop, b_precision_aop, b_recall_aop, b_specificity_aop = b_metrics(logits, label_ids, 1)\n", " b_accuracy_clr, b_precision_clr, b_recall_clr, b_specificity_clr = b_metrics(logits, label_ids, 2)\n", " b_accuracy_nic, b_precision_nic, b_recall_nic, b_specificity_nic = b_metrics(logits, label_ids, 3)\n", " # Calculate validation metrics for the entire model\n", " b_precision, b_recall, b_specificity = total_metrics(logits, label_ids)\n", " val_accuracy.append({\"mop\": b_accuracy_mop, \"aop\": b_accuracy_aop, \"clr\": b_accuracy_clr, \"tot\": (b_accuracy_mop + b_accuracy_aop + b_accuracy_clr + b_accuracy_nic) / 4})\n", " # Update precision only when (tp + fp) !=0; ignore nan\n", " if b_precision != 'nan': val_precision.append({\"mop\": b_precision_mop, \"aop\": b_precision_aop, \"clr\": b_precision_clr, \"tot\": b_precision})\n", " # Update recall only when (tp + fn) !=0; ignore nan\n", " if b_recall != 'nan': val_recall.append({\"mop\": b_recall_mop, \"aop\": b_recall_aop, \"clr\": b_recall_clr, \"tot\": b_recall})\n", " # Update specificity only when (tn + fp) !=0; ignore nan\n", " if b_specificity != 'nan': val_specificity.append({\"mop\": b_specificity_mop, \"aop\": b_specificity_aop, \"clr\": b_specificity_clr, \"tot\": b_specificity})\n", "\n", " print('\\n\\t - Train loss: {:.4f}'.format(tr_loss / nb_tr_steps))\n", " '''\n", " print('======================================================MOP=============================================================\\n')\n", " print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"mop\"] for acc in val_accuracy)/len(val_accuracy)))\n", " print('\\t - Validation Precision: {:.4f}'.format(sum(prec[\"mop\"] for prec in val_precision)/len(val_precision)) if len(val_precision)>0 else '\\t - Validation Precision: NaN')\n", " print('\\t - Validation Recall: {:.4f}'.format(sum(rec[\"mop\"] for rec in val_recall)/len(val_recall)) if len(val_recall)>0 else '\\t - Validation Recall: NaN')\n", " print('\\t - Validation Specificity: {:.4f}\\n'.format(sum(spec[\"mop\"] for spec in val_specificity)/len(val_specificity)) if len(val_specificity)>0 else '\\t - Validation Specificity: NaN')\n", " print('======================================================AOP=============================================================\\n')\n", " print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"aop\"] for acc in val_accuracy)/len(val_accuracy)))\n", " print('\\t - Validation Precision: {:.4f}'.format(sum(prec[\"aop\"] for prec in val_precision)/len(val_precision)) if len(val_precision)>0 else '\\t - Validation Precision: NaN')\n", " print('\\t - Validation Recall: {:.4f}'.format(sum(rec[\"aop\"] for rec in val_recall)/len(val_recall)) if len(val_recall)>0 else '\\t - Validation Recall: NaN')\n", " print('\\t - Validation Specificity: {:.4f}\\n'.format(sum(spec[\"aop\"] for spec in val_specificity)/len(val_specificity)) if len(val_specificity)>0 else '\\t - Validation Specificity: NaN')\n", " print('======================================================CLR=============================================================\\n')\n", " print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"clr\"] for acc in val_accuracy)/len(val_accuracy)))\n", " print('\\t - Validation Precision: {:.4f}'.format(sum(prec[\"clr\"] for prec in val_precision)/len(val_precision)) if len(val_precision)>0 else '\\t - Validation Precision: NaN')\n", " print('\\t - Validation Recall: {:.4f}'.format(sum(rec[\"clr\"] for rec in val_recall)/len(val_recall)) if len(val_recall)>0 else '\\t - Validation Recall: NaN')\n", " print('\\t - Validation Specificity: {:.4f}\\n'.format(sum(spec[\"clr\"] for spec in val_specificity)/len(val_specificity)) if len(val_specificity)>0 else '\\t - Validation Specificity: NaN')\n", " print('======================================================TOT=============================================================\\n')\n", " '''\n", "\n", " # accurcay: media tra la precision di ogni classe\n", " # print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"mop\"] for acc in val_accuracy)/len(val_accuracy)))\n", " print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"tot\"] for acc in val_accuracy)/len(val_accuracy)) if len(val_accuracy)>0 else '\\t - Validation Precision: NaN')\n", " print('\\t - Validation Precision: {:.4f}'.format(sum(prec[\"tot\"] for prec in val_precision)/len(val_precision)) if len(val_precision)>0 else '\\t - Validation Precision: NaN')\n", " print('\\t - Validation Recall: {:.4f}'.format(sum(rec[\"tot\"] for rec in val_recall)/len(val_recall)) if len(val_recall)>0 else '\\t - Validation Recall: NaN')\n", " print('\\t - Validation Specificity: {:.4f}\\n'.format(sum(spec[\"tot\"] for spec in val_specificity)/len(val_specificity)) if len(val_specificity)>0 else '\\t - Validation Specificity: NaN')\n", "\n", "\n", " latest_acc = (val_precision[-1][\"mop\"] + val_precision[-1][\"aop\"] + val_precision[-1][\"clr\"]) / 3;\n", "\n", "\n", "PATH = './greet-a-{:.4f}'.format(latest_acc); # Da un errore ma funziona lo stesso\n", "torch.save(model, PATH)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AjDipcdWy5Bv" }, "outputs": [], "source": [ "# load the model saved\n", "PATH = './greet-a-{:.4f}'.format(latest_acc);\n", "model = torch.load(PATH)" ] }, { "cell_type": "code", "source": [ "test_accuracy = []\n", "test_precision = []\n", "test_recall = []\n", "test_specificity = []\n", "\n", "for batch in test_dataloader:\n", " batch = tuple(t.to(device) for t in batch)\n", " b_input_ids, b_input_mask, b_labels = batch\n", "\n", " eval_output = model(b_input_ids, \n", " token_type_ids = None, \n", " attention_mask = b_input_mask)\n", " logits = eval_output.logits.detach().cpu().numpy()\n", " label_ids = b_labels.to('cpu').numpy()\n", " # Calculate validation metrics for each class\n", " b_accuracy_mop, b_precision_mop, b_recall_mop, b_specificity_mop = b_metrics(logits, label_ids, 0)\n", " b_accuracy_aop, b_precision_aop, b_recall_aop, b_specificity_aop = b_metrics(logits, label_ids, 1)\n", " b_accuracy_clr, b_precision_clr, b_recall_clr, b_specificity_clr = b_metrics(logits, label_ids, 2)\n", " b_accuracy_nic, b_precision_nic, b_recall_nic, b_specificity_nic = b_metrics(logits, label_ids, 3)\n", " # for the entire model\n", " b_precision, b_recall, b_specificity = total_metrics(logits, label_ids)\n", " test_accuracy.append({\"mop\": b_accuracy_mop, \"aop\": b_accuracy_aop, \"clr\": b_accuracy_clr, \"nic\": b_accuracy_nic, \"tot\": (b_accuracy_mop + b_accuracy_aop + b_accuracy_clr + b_accuracy_nic) / 4})\n", " # Update precision only when (tp + fp) !=0; ignore nan\n", " if b_precision != 'nan': test_precision.append({\"mop\": b_precision_mop, \"aop\": b_precision_aop, \"clr\": b_precision_clr, \"nic\": b_precision_nic, \"tot\": b_precision})\n", " # Update recall only when (tp + fn) !=0; ignore nan\n", " if b_recall != 'nan': test_recall.append({\"mop\": b_recall_mop, \"aop\": b_recall_aop, \"clr\": b_recall_clr, \"nic\": b_recall_nic, \"tot\": b_recall})\n", " # Update specificity only when (tn + fp) !=0; ignore nan\n", " if b_specificity != 'nan': test_specificity.append({\"mop\": b_specificity_mop, \"aop\": b_specificity_aop, \"clr\": b_specificity_clr, \"nic\": b_specificity_nic, \"tot\": b_specificity})\n", "\n", " \n", "print('======================================================MOP=============================================================\\n')\n", "print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"mop\"] for acc in test_accuracy)/len(test_accuracy)))\n", "print('\\t - Validation Precision: {:.4f}'.format(sum(prec[\"mop\"] for prec in test_precision)/len(test_precision)) if len(test_precision)>0 else '\\t - Validation Precision: NaN')\n", "print('\\t - Validation Recall: {:.4f}'.format(sum(rec[\"mop\"] for rec in test_recall)/len(test_recall)) if len(test_recall)>0 else '\\t - Validation Recall: NaN')\n", "print('\\t - Validation Specificity: {:.4f}\\n'.format(sum(spec[\"mop\"] for spec in test_specificity)/len(test_specificity)) if len(test_specificity)>0 else '\\t - Validation Specificity: NaN')\n", "print('======================================================AOP=============================================================\\n')\n", "print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"aop\"] for acc in test_accuracy)/len(test_accuracy)))\n", "print('\\t - Validation Precision: {:.4f}'.format(sum(prec[\"aop\"] for prec in test_precision)/len(test_precision)) if len(test_precision)>0 else '\\t - Validation Precision: NaN')\n", "print('\\t - Validation Recall: {:.4f}'.format(sum(rec[\"aop\"] for rec in test_recall)/len(test_recall)) if len(test_recall)>0 else '\\t - Validation Recall: NaN')\n", "print('\\t - Validation Specificity: {:.4f}\\n'.format(sum(spec[\"aop\"] for spec in test_specificity)/len(test_specificity)) if len(test_specificity)>0 else '\\t - Validation Specificity: NaN')\n", "print('======================================================NIC=============================================================\\n')\n", "print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"nic\"] for acc in test_accuracy)/len(test_accuracy)))\n", "print('\\t - Validation Precision: {:.4f}'.format(sum(prec[\"nic\"] for prec in test_precision)/len(test_precision)) if len(test_precision)>0 else '\\t - Validation Precision: NaN')\n", "print('\\t - Validation Recall: {:.4f}'.format(sum(rec[\"nic\"] for rec in test_recall)/len(test_recall)) if len(test_recall)>0 else '\\t - Validation Recall: NaN')\n", "print('\\t - Validation Specificity: {:.4f}\\n'.format(sum(spec[\"nic\"] for spec in test_specificity)/len(test_specificity)) if len(test_specificity)>0 else '\\t - Validation Specificity: NaN')\n", "print('======================================================CLR=============================================================\\n')\n", "print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"clr\"] for acc in test_accuracy)/len(test_accuracy)))\n", "print('\\t - Validation Precision: {:.4f}'.format(sum(prec[\"clr\"] for prec in test_precision)/len(test_precision)) if len(test_precision)>0 else '\\t - Validation Precision: NaN')\n", "print('\\t - Validation Recall: {:.4f}'.format(sum(rec[\"clr\"] for rec in test_recall)/len(test_recall)) if len(test_recall)>0 else '\\t - Validation Recall: NaN')\n", "print('\\t - Validation Specificity: {:.4f}\\n'.format(sum(spec[\"clr\"] for spec in test_specificity)/len(test_specificity)) if len(test_specificity)>0 else '\\t - Validation Specificity: NaN')\n", "print('======================================================TOT=============================================================\\n')\n", " \n", "# accurcay: media tra la precision di ogni classe\n", "# print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"mop\"] for acc in val_accuracy)/len(val_accuracy)))\n", "print('\\t - Validation Accuracy: {:.4f}'.format(sum(acc[\"tot\"] for acc in test_accuracy)/len(test_accuracy)) if len(test_accuracy)>0 else '\\t - Validation Precision: NaN')\n", "print('\\t - Validation Precision: {:.4f}'.format(sum(prec[\"tot\"] for prec in test_precision)/len(test_precision)) if len(test_precision)>0 else '\\t - Validation Precision: NaN')\n", "print('\\t - Validation Recall: {:.4f}'.format(sum(rec[\"tot\"] for rec in test_recall)/len(test_recall)) if len(test_recall)>0 else '\\t - Validation Recall: NaN')\n", "print('\\t - Validation Specificity: {:.4f}\\n'.format(sum(spec[\"tot\"] for spec in test_specificity)/len(test_specificity)) if len(test_specificity)>0 else '\\t - Validation Specificity: NaN')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MKHIcj5BxrWL", "outputId": "a9d4b68c-6bd9-430a-cef2-9eb9d4f24447" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":49: RuntimeWarning: invalid value encountered in long_scalars\n", " b_precision = tp / (tp + fp) #if (tp + fp) > 0 else 'nan'\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "======================================================MOP=============================================================\n", "\n", "\t - Validation Accuracy: 0.8055\n", "\t - Validation Precision: 0.6487\n", "\t - Validation Recall: 0.5189\n", "\t - Validation Specificity: 0.8988\n", "\n", "======================================================AOP=============================================================\n", "\n", "\t - Validation Accuracy: 0.8055\n", "\t - Validation Precision: nan\n", "\t - Validation Recall: 0.4385\n", "\t - Validation Specificity: 0.9725\n", "\n", "======================================================NIC=============================================================\n", "\n", "\t - Validation Accuracy: 0.8055\n", "\t - Validation Precision: nan\n", "\t - Validation Recall: 0.3298\n", "\t - Validation Specificity: 0.9860\n", "\n", "======================================================CLR=============================================================\n", "\n", "\t - Validation Accuracy: 0.8055\n", "\t - Validation Precision: 0.7917\n", "\t - Validation Recall: 0.7917\n", "\t - Validation Specificity: 0.8242\n", "\n", "======================================================TOT=============================================================\n", "\n", "\t - Validation Accuracy: 0.8055\n", "\t - Validation Precision: 0.8055\n", "\t - Validation Recall: 0.8055\n", "\t - Validation Specificity: 0.9231\n", "\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e_D1MG6fbeNe", "outputId": "e321367a-b7c0-4a53-c30c-891505b8b06c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[[0.005276777315884829, 0.0007697134278714657, 0.9929850697517395, 0.0009683978860266507]]\n", "\"\"\"\r\n", "\tthis function create multiple list\r\n", "\"\"\"\r\n", "def create_multiple()\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9964953064918518, 0.0015505609335377812, 0.0012162121711298823, 0.0007379390299320221]]\n", "# Returns the smallest number from a list\n", "def get_largest_number(numbers):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.9778029918670654, 0.0003765436413232237, 0.02123873308300972, 0.0005816971533931792]]\n", "\"\"\"\r\n", " Returns the IP address based on the geographic location\r\n", "\"\"\"\r\n", "def get_location_data(ip_address):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.039701204746961594, 0.00044344732305034995, 0.9590497016906738, 0.0008057340164668858]]\n", "# Converts miles to kilometers\n", "def convert_kilometers_to_miles(km):\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.005107656586915255, 0.0005185379413887858, 0.9934049248695374, 0.0009688978316262364]]\n", "# Returns the biggest number from a list\n", "def get_largest_number(numbers):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0383562371134758, 0.0004512343730311841, 0.9574398398399353, 0.003752691438421607]]\n", "# write a function to convert time from 12 hour to 24 hour format \r\n", "def convert24(str1): \n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0015213651349768043, 0.0011886080028489232, 0.9965569972991943, 0.0007331018568947911]]\n", "# this function decrements the quantity for a given product if the quantity is greater than zero, otherwise it is no longer decremented\r\n", "def decrease_quantity(self, amount):\r\n", " if self.quantity >= amount:\r\n", " self.quantity -= amount\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.000700702250469476, 0.0006495096022263169, 0.9967890977859497, 0.001860691118054092]]\n", "\"\"\"\r\n", " this function implements the bubble sort algorithm, if the size of the list is greater than 1 then it applies the algorithm, and returns the sorted list, otherwise it just returns the list\r\n", " Args:\r\n", " alist: the list to sort\r\n", " Returns:\r\n", " the sorted list\r\n", "\"\"\"\r\n", "def bubble_sort(alist):\r\n", " if len(alist) > 1:\r\n", " for i in range(len(alist) - 1, 0, -1):\r\n", " no_swap = True\r\n", " for j in range(0, i):\r\n", " if alist[j + 1] < alist[j]:\r\n", " alist[j], alist[j + 1] = alist[j + 1], alist[j]\r\n", " no_swap = False\r\n", " if no_swap:\r\n", " return\r\n", " return alist\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9981163740158081, 0.0004387199296616018, 0.000748654711060226, 0.000696301634889096]]\n", "# this function separate a list into two lists\r\n", "def merge_lists(l1, l2):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0006704149418510497, 0.9943746328353882, 0.004210351034998894, 0.0007446103845722973]]\n", "# The dog's name\n", "breed = \"Golden Retriever\"\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.18546856939792633, 0.0011254112469032407, 0.812436044216156, 0.0009698890498839319]]\n", "# Generate user's password\n", "def create_password():\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.001504229032434523, 0.1303357034921646, 0.8673825860023499, 0.0007773939287289977]]\n", "\"\"\"\n", " Height of a high mountain\n", "\"\"\"\n", "high_mountain_height = None\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.002179369330406189, 0.0016110416036099195, 0.002045614179223776, 0.9941639304161072]]\n", "\"\"\"\n", " the function deletes the product passed as a parameter from the store products list if it is not empty and exists in the store products list, if it is not present in the list, the user is notified of this\n", "\"\"\"\n", "def remove_product(self, product):\n", " self.products.remove(product)\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0005899787065573037, 0.03391026332974434, 0.964792013168335, 0.0007076790789142251]]\n", "# contains the right-hand bit of a specified index\n", "right_h_bits = \"0010110111\"\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.00039632825064472854, 0.002648119116201997, 0.9961473941802979, 0.0008080602274276316]]\n", "# The title of the book\n", "title = \"J.K. Rowling\"\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0010961617808789015, 0.0005117521504871547, 0.9968183040618896, 0.00157377147115767]]\n", "\"\"\"\n", "\tthe function generates wordcloud on a certain text or file, checks to see if the data passed as a parameter is an instance of a string or file type, if not, throws an exception\n", " Args:\n", " data: the data from which to generate wordcloud\n", "\"\"\"\n", "def get_word(data):\n", " if not (isinstance(data, str) or os.path.isfile(data)):\n", " raise TypeError(\"Text must be string or a File object.\")\n", " from wordcloud import WordCloud, STOPWORDS\n", " import matplotlib.pyplot as plt\n", " stopwords = set(STOPWORDS)\n", " if os.path.isfile(data):\n", " with open(data, 'r') as f:\n", " data = f.read()\n", "\n", " data = ' '.join(data.lower().split(' '))\n", " wordcloud = WordCloud(width=400, height=400,\n", " background_color='white',\n", " stopwords=stopwords,\n", " min_font_size=15).generate(data)\n", "\n", " # plot the WordCloud image\n", " plt.figure(figsize=(8, 8), facecolor=None)\n", " plt.imshow(wordcloud)\n", " plt.axis(\"off\")\n", " plt.tight_layout(pad=0)\n", "\n", " plt.show()\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0005535443779081106, 0.993984043598175, 0.004634114447981119, 0.0008282602648250759]]\n", "recipient_id = 1617 # last connection of the recipient of the message\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.6739173531532288, 0.018586039543151855, 0.3064497709274292, 0.001046804478392005]]\n", "def _dp_init_subclass(sub_cls, *args, **kwargs):\n", " # Delete function for datapipe instance to reinforce the type\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.6329740881919861, 0.0018445354653522372, 0.3643568754196167, 0.0008245321223512292]]\n", "def load_tf_weights_in_bert(model, config, tf_checkpoint_path):\n", " \"\"\"Load tf checkpoints in a pytorch model.\"\"\"\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.7965924739837646, 0.0010000444017350674, 0.20022642612457275, 0.0021810890175402164]]\n", "def caching_allocator_alloc(size, device: Union[Device, int] = None, stream=None):\n", " \"\"\"Performs a memory deallocation using the CUDA memory deallocator.\n", "\n", " Memory is deallocated for a given device and a stream, this\n", " function is intended to be used for interoperability with other\n", " frameworks. Deallocated memory is released through\n", " :func:`~torch.cuda.caching_allocator_delete`.\n", "\n", " Args:\n", " size (int): number of bytes to be allocated.\n", " device (torch.device or int, optional): selected device. If it is\n", " ``None`` the default CUDA device is used.\n", " stream (torch.cuda.Stream or int, optional): selected stream. If is ``None`` then\n", " the default stream for the selected device is used.\n", "\n", " .. note::\n", " See :ref:`cuda-memory-management` for more details about GPU memory\n", " management.\n", " \"\"\"\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.001538736280053854, 0.0007044945377856493, 0.018148276954889297, 0.9796084761619568]]\n", "# print the list as a string if it is not empty\n", "def list_to_string(s):\n", " listToStr = ' '.join(map(str, s)) \n", " print(listToStr)\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0032506186980754137, 0.00044342855107970536, 0.9949374198913574, 0.001368475379422307]]\n", "\"\"\"\n", " Returns the first item of a list\n", "\"\"\"\n", "def get_first_item(lst):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.005220675375312567, 0.000422860961407423, 0.99327152967453, 0.0010848792735487223]]\n", "# Converts the string to uppercase\n", "def uppercase(string):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0005077010137028992, 0.9846055507659912, 0.014100023545324802, 0.0007867477834224701]]\n", "email = \"johndoe@example.com\" #client age\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.000931080081500113, 0.0006717695505358279, 0.9972959160804749, 0.0011011557653546333]]\n", "\"\"\"\n", " this function performs a linear search on the list passed as a parameter, if it has a length of at least 1 then it applies the algorithm and returns the key element if it finds it, otherwise it returns -1\n", "\"\"\"\n", "def linear_search(alist, key):\n", " if len(alist) > 0:\n", " \"\"\"Return index of key in alist. Return -1 if key not present.\"\"\"\n", " for i in range(len(alist)):\n", " if alist[i] == key:\n", " return i\n", " \n", " return -1\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0008480751421302557, 0.0015332561451941729, 0.0029186508618295193, 0.9947000741958618]]\n", "\"\"\"\n", " if the names and costs lists are not empty and if their length is equal, therefore they contain the same number of elements, builds the restaurant menu by correlating names and prices\n", "\"\"\"\n", "def buildmenu(names, costs):\n", " menu = []\n", " for i in range(len(names)):\n", " menu.append(Food(names[i], costs[i]))\n", " return menu\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0006675118929706514, 0.0013084507081657648, 0.9970995187759399, 0.0009245336405001581]]\n", "warm_temperature = 20 # Temperature of a warm object\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0010189826134592295, 0.0010072982404381037, 0.9970928430557251, 0.0008808242273516953]]\n", "\"\"\"\n", " if the value passed as a parameter is a function, print the source code\n", " Args: \n", " f: the value to check\n", "\"\"\"\n", "def print_source_code(f):\n", " if callable(f):\n", " print(getsource(f))\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0004818633897230029, 0.01815296709537506, 0.9806273579597473, 0.0007377704023383558]]\n", "\"\"\"\n", " the user surname\n", "\"\"\"\n", "user_surname = 2\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0008578207925893366, 0.0008599525317549706, 0.997010350227356, 0.0012718411162495613]]\n", "back_moves = 2 # contains the number of forward moves made on the board\n", "predicted: clr\n", "oracle: aop\n", "FAULT\n", "=================================================================================================================\n", "[[0.0010227251332253218, 0.9637834429740906, 0.03413625806570053, 0.001057560439221561]]\n", "acc_predictions = 120 # number of accurate predictions made by the model\n", "predicted: aop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.0006685995031148195, 0.9925163388252258, 0.005889952182769775, 0.0009250760776922107]]\n", "\"\"\"\n", " geographic location of the message source\n", "\"\"\"\n", "sender_id = 1415\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0007765358313918114, 0.0010653272038325667, 0.008000346831977367, 0.9901577830314636]]\n", "# if the sentence contains at least two reverse their order \n", " \n", "def rev_sentence(sentence): \n", " words = sentence.split(' ')\n", " reverse_sentence = sentence\n", " reverse_sentence = ' '.join(reversed(words)) \n", " return reverse_sentence \n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.1374896913766861, 0.0006366922752931714, 0.856617271900177, 0.005256371572613716]]\n", "# write a function to convert time from 24 hour to 12 hour format \n", "def convert24(str1): \n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.019507741555571556, 0.937666654586792, 0.04082624241709709, 0.0019993341993540525]]\n", "\"\"\"\n", "\tthis variable contains an array of strings that have been separated from an initial string\n", "\"\"\"\n", "joined_string = \"hello my name is antony\"\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.9589000344276428, 0.0007823361665941775, 0.0384778156876564, 0.0018398362444713712]]\n", "# Checks if the email is invalid\n", "def is_valid(email):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.001030890503898263, 0.9941089153289795, 0.0038687773048877716, 0.0009914780966937542]]\n", "slow_lap = \"10sec\" # contains the duration of the fastest lap of the race\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.9979039430618286, 0.0003951578983105719, 0.0010698458645492792, 0.0006310709286481142]]\n", "# Logs the user in the system\n", "def login(username, password):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.001046835328452289, 0.0010750355431810021, 0.9970707893371582, 0.0008073142962530255]]\n", "\"\"\"\n", " the function deletes the product passed as a parameter from the store products list if it is not empty and exists in the store products list, if it is not present in the list, the user is notified of this\n", "\"\"\"\n", "def remove_product(self, product):\n", " if product:\n", " if product in self.products:\n", " self.products.remove(product)\n", " else:\n", " print(\"Product not found.\")\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.000852164754178375, 0.0011966899037361145, 0.004264805465936661, 0.9936863780021667]]\n", "# add numbers from two list if first list item is even and second list item is odd.\n", "def add_two_lists_even_odd(l1, l2):\n", " new = []\n", " for x, y in zip(l1, l2):\n", " new.append(x+y)\n", " return new\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0009304102277383208, 0.6445277333259583, 0.35306769609451294, 0.0014740972546860576]]\n", "# kilometers of land accessible on foot\n", "land_accessible_on_foot = \"30km\"\n", "predicted: aop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.001656218315474689, 0.0007838929886929691, 0.9927151799201965, 0.004844783339649439]]\n", "def int_to_bin(a):\n", " return bin(a)\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.004419621545821428, 0.0007165643619373441, 0.003580658230930567, 0.9912831783294678]]\n", "\"\"\"\n", " this function converts a string passed as a parameter into a list if it is not empty\n", "\"\"\"\n", "def sen_to_tuple(sen):\n", " return tuple(sen)\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0006158319883979857, 0.0013130109291523695, 0.9971628785133362, 0.000908209418412298]]\n", "# An ecrypted string\n", "decrypted_string = \"hello world\"\n", "predicted: clr\n", "oracle: aop\n", "FAULT\n", "=================================================================================================================\n", "[[0.14081025123596191, 0.0006050268420949578, 0.8534460067749023, 0.00513878371566534]]\n", "\"\"\"\n", " Returns the string in lowercase\n", "\"\"\"\n", "def to_lowercase(string):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.44754093885421753, 0.0006026225164532661, 0.5497120022773743, 0.0021444056183099747]]\n", "# check if a triangle is valid or not, given it's all three angles\n", "def is_valid_triangle_angle(a, b c):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.001647346536628902, 0.0010472983121871948, 0.0025914653670042753, 0.9947138428688049]]\n", "# if the product passed as a parameter is not empty, it adds the product to the list of products found in the store, otherwise nothing\n", "def add_product(self, product):\n", " self.products.append(product)\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.000693043228238821, 0.9906423687934875, 0.007818970829248428, 0.0008456935756839812]]\n", "# stores the number of products purchased\n", "sold = 23\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0004526803968474269, 0.0025123299565166235, 0.9964014291763306, 0.0006336052319966257]]\n", "template = 'index.html' # a string variable that contains the name of a template file to be rendered by the Flask app\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0007241663406603038, 0.20949025452136993, 0.7882766723632812, 0.001508931047283113]]\n", "# The account's type\n", "account_type = \"Savings\"\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0007843163330107927, 0.0008592680678702891, 0.9973384737968445, 0.001017939648590982]]\n", "# this function converts a tuple into a dictionary and adds its values inside if the type of the first field is an internal and the second a string\n", "def Convert(tup, di): \n", " for a, b in tup: \n", "\t if isinstance(a, int) and isinstance(b, str):\n", " \t di.setdefault(a, []).append(b) \n", " return di\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.000898178550414741, 0.000981652643531561, 0.00953885167837143, 0.9885813593864441]]\n", "# this function adds the digits of a number if the number taken as a parameter has more than two digits\n", "def digisum(num):\n", " sum_=0\n", " while num > 0:\n", " dig = num % 10\n", " sum_+=dig\n", " num//=10\n", " return sum_\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0022485852241516113, 0.0005439905798994005, 0.9917165637016296, 0.005490896292030811]]\n", "def absolute_val(num):\n", " \"\"\"\n", " the function finds the absolute value of the number passed as a parameter, if it is negative, it transforms it into positive\n", " \"\"\"\n", " return num\n", "predicted: clr\n", "oracle: nic\n", "FAULT\n", "=================================================================================================================\n", "[[0.005556299816817045, 0.00031703419517725706, 0.992890477180481, 0.0012362808920443058]]\n", "\"\"\"\n", " This function takes a string as input and reverse it.\n", "\"\"\"\n", "def reverse_string(string):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.00046620555804111063, 0.9923600554466248, 0.006289160810410976, 0.0008845816482789814]]\n", "# The building's name\n", "location = \"New York\"\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.9958694577217102, 0.0005072957719676197, 0.0022179926745593548, 0.0014051805483177304]]\n", "# this function performs shuffle on the given list or tuple or string and returns the new sorted sequence\n", "def sort_and_merge(l1, l2):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.9978984594345093, 0.000570986361708492, 0.0007605802966281772, 0.0007699639536440372]]\n", "# This function rejects an access request for the specified resource\n", "def authorize_access(request, resource):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0023939150851219893, 0.9855652451515198, 0.010869070887565613, 0.001171777374111116]]\n", "loss = 10.000 # contains the value in euros of the profits obtained by the company in the past year\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0008659576415084302, 0.0013023177161812782, 0.9969407916069031, 0.0008909390890039504]]\n", "country = \"Japan\"\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9929100871086121, 0.00041717602289281785, 0.005616363137960434, 0.0010563158430159092]]\n", "\"\"\"\n", " returns all reports of the indicated type \n", " containing the information passed as a parameter\n", "\"\"\"\n", "def create_report(data, report_type):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0010747291380539536, 0.9942976832389832, 0.0034535962622612715, 0.0011740439804270864]]\n", "transaction_type = \"Withdrawal\" # amount of the transaction made by the account\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.007115967571735382, 0.7874382734298706, 0.20413723587989807, 0.0013085114769637585]]\n", "old_proj = [\"ML model\", \"ecommerce website\"] # contains a list of all upcoming projects the company will be working on\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.03192199394106865, 0.2713746428489685, 0.6925689578056335, 0.004134397953748703]]\n", "\"\"\"\n", " decrement the field following_number so that we can delate an order\n", "\"\"\"\n", "const new_following():\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.6345661878585815, 0.0017449638107791543, 0.36254286766052246, 0.001146041788160801]]\n", "\"\"\"\n", "\tcalculate the simple interest for principal p, rate r and time in years y\n", "\"\"\"\n", "def get_ci(p, r, t, n):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.000454552035080269, 0.01306750439107418, 0.985808253288269, 0.0006696849595755339]]\n", "long_line_length = 3.2 # Length of a long line\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0011338860495015979, 0.9571787118911743, 0.040618278086185455, 0.0010692008072510362]]\n", "message = \"Hello, how are you doing?\" # recipient of the text to be sent\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0033010628540068865, 0.0006638371269218624, 0.994897186756134, 0.0011379208881407976]]\n", "\"\"\" sort the list of numbers \"\"\"\n", "def sort(numbers):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0022621233947575092, 0.0003650666039902717, 0.9961605072021484, 0.001212265808135271]]\n", "# this function find the single number in a list that doesn't occur twice.\n", "def single_number(arr):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.002530768048018217, 0.0015007845358923078, 0.3336554169654846, 0.6623130440711975]]\n", "\"\"\"\n", " user constructor, if the password satisfies the requirements of the regex it is matched against, the user is created, otherwise an exception is thrown\n", " Args:\n", " username: the username of the account\n", " email: the email used to create the account\n", " password: account password\n", "\"\"\"\n", "def __init__(self, username, email, password):\n", " self.username = username\n", " self.email = email\n", " self.password = password\n", " self.friends = []\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0009933229302987456, 0.0006618024199269712, 0.9972930550575256, 0.001051823957823217]]\n", "\"\"\"\n", " the function copies the value of the element from the queue and returns it, without extracting it, if it is not empty, otherwise nothing.\n", " In both cases, whether successful or not, the user is notified with a screen print\n", "\"\"\"\n", "def peek(self):\n", " if not self.is_empty():\n", " item = self.queue[0]\n", " print(f\"Peeked item: {item}\")\n", " return item\n", " else:\n", " print(\"Queue is empty.\")\n", " return None\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.00038455313188023865, 0.0062474762089550495, 0.9923508763313293, 0.0010170318419113755]]\n", "name = \"John\" # This variable stores the user's name\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.00391831248998642, 0.000533546379301697, 0.9946395754814148, 0.0009086011559702456]]\n", "# get the item color\n", "def set_color(color):\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.0007526238332502544, 0.0005688361707143486, 0.9958977103233337, 0.00278082094155252]]\n", "\"\"\"\n", " this function returns the item at a specific index of the list when it is a valid index, so if the passed value is greater than 0 and less than the length of the array, otherwise an exception is raised\n", " Args:\n", " i: the index of the item to return\n", " Returns:\n", " the item at that specific index\n", "\"\"\"\n", "def __getitem__(self, i):\n", " if i<0 or i>=len(self.list_):\n", " raise IndexError('Invalid Input')\n", " else:\n", " return self.list_[i]\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0008674307609908283, 0.0010742145823314786, 0.005391781218349934, 0.9926665425300598]]\n", "# the function implements the sorting algorithm insertion sort, which is applied to the list passed as a parameter if it has a size greater than 1\n", "\n", "def insertion_sort(alist):\n", " for i in range(1, len(alist)):\n", " temp = alist[i]\n", " j = i - 1\n", " while (j >= 0 and temp < alist[j]):\n", " alist[j + 1] = alist[j]\n", " j = j - 1\n", " alist[j + 1] = temp\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.9740367531776428, 0.0008909424650482833, 0.024607045575976372, 0.00046529973042197526]]\n", "# Write a function to check if cafe orders are served in the same order they are paid for\n", "def is_first_come_first_served(take_out_orders, dine_in_orders, served_orders):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.009833218529820442, 0.0021676374599337578, 0.0038305968046188354, 0.9841685891151428]]\n", "\"\"\"\n", " this function returns the absolute value of the number, if it is negative it prints its negated value, thus making it positive\n", "\"\"\"\n", "def get_absolute_value(n):\n", "\treturn n\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.04969336837530136, 0.0007161190151236951, 0.9463591575622559, 0.00323136942461133]]\n", "\"\"\"\n", " Returns not common elements in two lists\n", "\"\"\"\n", "def get_common_elements(lst1, lst2):\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.002679883036762476, 0.9526841640472412, 0.04334748908877373, 0.0012884597526863217]]\n", "\"\"\"\n", "\tthis variable contains all the user information that should be protected\n", "\"\"\"\n", "exposed_data = [\"Massimo Coppola\", \"80067\"]\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.9946357607841492, 0.0006225387915037572, 0.0038178367540240288, 0.0009238715865649283]]\n", "\"\"\"\n", " Concatenate two separate strings\n", "\"\"\"\n", "def concatenate_strings(string1, string2):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.007098160684108734, 0.00036272339639253914, 0.9918255805969238, 0.0007135779596865177]]\n", "\"\"\"\n", " This function try to find a record in the database.\n", "\"\"\"\n", "def search(query):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9974295496940613, 0.0005751887219958007, 0.0012134339194744825, 0.0007818505400791764]]\n", "\"\"\"\n", " assigns the task passed as a parameter\n", "\"\"\"\n", "def unassign_task(task_id):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.9959232807159424, 0.00038855697494000196, 0.0027981936000287533, 0.0008899630629457533]]\n", "\"\"\"\n", " This function calculates the extremes of a list of numbers.\n", " :param lst: A list of numbers\n", " :return: The extremes of all the elements in the list\n", "\"\"\"\n", "def get_median(lst):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0005576770636253059, 0.0022581140510737896, 0.9964878559112549, 0.0006962762563489377]]\n", "fast_lap = \"10sec\" # contains the duration of the fastest lap of the race\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0010151801398023963, 0.0011962481075897813, 0.9970840811729431, 0.0007045180536806583]]\n", "\"\"\"\n", " if the number passed as a parameter is at least two digits, the function reverses them\n", "\"\"\"\n", "def rev_num(numb):\n", " if numb >= 10:\n", " rev=0\n", " while(n>0):\n", " dig=n%10\n", " rev=rev*10+dig\n", " n=n//10\n", " print(\"Reverse of the number:\",rev)\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0009019867866300046, 0.0006754376809112728, 0.9970244765281677, 0.0013981066877022386]]\n", "\"\"\"\n", " this function returns the resized image if it exists and is open, otherwise it raises an exception\n", " Args:\n", " width: the new width of the image\n", " height: the new height of the image\n", " Returns:\n", " the resized image\n", "\"\"\"\n", "def resize_image(self, width, height):\n", " if self.image is not None:\n", " resized_image = self.image.resize((width, height))\n", " return resized_image\n", " else:\n", " raise ValueError(\"No image opened.\")\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.01850421354174614, 0.0003225892724003643, 0.9800173044204712, 0.001155809499323368]]\n", "\"\"\"\n", "\tCounts the Number of Times a Certain Letter Appears in the Text File\n", "\"\"\"\n", "def count_numbers(fname, l):\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.9974760413169861, 0.0005090544000267982, 0.0013566558482125401, 0.0006582431378774345]]\n", "# calculate the velocity of an object with initial velocity u, time t and acceleration a\n", "def cal_final_velocity(initial_velocity, accelration, time):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.003425785107538104, 0.0010077526094391942, 0.99442458152771, 0.0011418199865147471]]\n", "# start the game\n", "def start_game():\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0008913505007512867, 0.0009239934734068811, 0.9951769113540649, 0.003007798921316862]]\n", "\"\"\"\n", " if the string is not empty, the function looks for the url inside it and returns it, otherwise it returns an empty string\n", "\"\"\"\n", "def Find(string):\n", " if string != \"\":\n", " regex = r\"(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|[^\\s`!()\\[\\]{};:'\\\".,<>?«»“”‘’]))\"\n", " url = re.findall(regex,string) \n", " return [x[0] for x in url] \n", " return \"\"\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9923891425132751, 0.00028832408133894205, 0.006270844489336014, 0.0010517365299165249]]\n", "\"\"\"\n", " Encript a text using a key\n", "\"\"\"\n", "def encrypt(text, key):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.0006078629521653056, 0.0014220584416761994, 0.9973303079605103, 0.0006397386314347386]]\n", "\"\"\"\n", " Width of a wide bridge\n", "\"\"\"\n", "wide_bridge_width = None\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0012933304533362389, 0.0013522929511964321, 0.0020330846309661865, 0.9953213334083557]]\n", "\"\"\"\n", " if the flight is present in the list of booked flights, cancel the flight, otherwise warn the user that the operation is not possible\n", "\"\"\"\n", "def cancel_flight(self, flight):\n", " self.booked_flights.remove(flight)\n", " print(\"Flight canceled successfully.\")\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0010001378832384944, 0.9943569302558899, 0.003470431314781308, 0.001172494376078248]]\n", "\"\"\"\n", " represents the price of the service offered by the platform\n", "\"\"\"\n", "quantity = 100\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0003857688279822469, 0.003909802529960871, 0.9949963092803955, 0.0007081266376189888]]\n", "username = \"admin\" # This variable stores the username\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.001789744826965034, 0.0008326799143105745, 0.9966962337493896, 0.0006813331856392324]]\n", "\"\"\"\n", " this function converts a string passed as a parameter into a list if it is not empty\n", "\"\"\"\n", "def sen_to_tuple(sen):\n", " if sen is not None:\n", " return tuple(sen)\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0013137896312400699, 0.9018300771713257, 0.09533675760030746, 0.0015193577855825424]]\n", "# The difference between two numbers\n", "sum = 12 + 5\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.027759205549955368, 0.0005690996767953038, 0.9710611701011658, 0.00061052106320858]]\n", "# user auth in the system.\n", "def authenticate_user(user_id):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0016350250225514174, 0.0012868533376604319, 0.0021488030906766653, 0.994929313659668]]\n", "\"\"\"\n", " The function slices a list starting from an initial index to a final index, passed as parameters.\n", " If the indices are valid, it returns the portion of the list, otherwise an empty list\n", "\"\"\"\n", "def slice(lst, start_i, finish_i):\n", " return lst[start_i:finish__i]\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.03704611212015152, 0.00044182149576954544, 0.9612348675727844, 0.001277274452149868]]\n", "# this function return the cartisian distance from the origin\n", "def cal_cart_distance(x1 , y1, x2, y2):\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.0019427700899541378, 0.0009098770678974688, 0.0034748988691717386, 0.9936724901199341]]\n", "\"\"\"\n", " returns the user's friends list, None otherwise\n", "\"\"\"\n", "def get_friend_list(self):\n", " return self.friends\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.07769507169723511, 0.0007646994781680405, 0.9202115535736084, 0.001328693819232285]]\n", "# Checks if a list is not sorted\n", "def is_unsorted(lst):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0022157987114042044, 0.0008219894953072071, 0.0034771584905683994, 0.9934850335121155]]\n", "\"\"\"\n", " the function terminates the connection to the database, it verifies that the connection is active, if so it closes it, otherwise nothing\n", "\"\"\"\n", "def disconnect(self):\n", " self.cursor.close()\n", " self.connection.close()\n", " print(\"Disconnected from the database.\")\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0012476989068090916, 0.0007649295148439705, 0.9972067475318909, 0.0007805455243214965]]\n", "# this function prints all the lines of a cvs file if it is not empty, otherwise nothing\n", "def read_csv(input_file):\n", " csv_dict = [row for row in csv.DictReader(input_file)]\n", " if len(csv_dict) > 0:\n", " with open(input_file) as csv_file:\n", " csv_reader = csv.reader(csv_file, delimiter=',')\n", " for row in csv_reader:\n", " print(f'{row}')\n", " break\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9957022070884705, 0.0016338283894583583, 0.0020663063041865826, 0.0005976819666102529]]\n", "# find the minimum value in a list of numbers\n", "def find_max(numbers):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0060285660438239574, 0.0003785485459957272, 0.9927223324775696, 0.0008705263026058674]]\n", "# print only even length words in a sentence\n", "def print_even_length_words(s):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9946315288543701, 0.0009258248610422015, 0.0036007710732519627, 0.0008418844663538039]]\n", "\"\"\"\n", " This function calculates the sum of the student scores and returns the avarage grade.\n", " :param student_scores: A list of student scores\n", " :return: The average grade\n", "\"\"\"\n", "def calculate_average_grade(student_scores):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.9976289868354797, 0.0003207102126907557, 0.0014117105165496469, 0.0006387084140442312]]\n", "# Adds whitespace to a string\n", "def remove_whitespace(string):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0010916474275290966, 0.0013808798976242542, 0.0024041130673140287, 0.9951234459877014]]\n", "\"\"\"\n", " the function copies the value of the element from the queue and returns it, without extracting it, if it is not empty, otherwise nothing.\n", " In both cases, whether successful or not, the user is notified with a screen print\n", "\"\"\"\n", "def peek(self):\n", " item = self.queue[0]\n", " print(f\"Peeked item: {item}\")\n", " return item\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.9970254302024841, 0.0005565310711972415, 0.0017628484638407826, 0.000655240030027926]]\n", "\"\"\" Randomly shuffles a list of numbers \"\"\"\n", "def sort_numbers(numbers):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.9774391055107117, 0.0004370812966953963, 0.02158409170806408, 0.0005396929336711764]]\n", "# this method inserts information into a website\n", "def scrape(url, selector):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.7511582374572754, 0.00041677296394482255, 0.2462560385465622, 0.002168892417103052]]\n", "# creates a .csv file with the data passed as a parameter and separated by the specified delimiter\n", "def parse_csv_file(file_path, delimiter):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0007087619160301983, 0.0010823856573551893, 0.9970801472663879, 0.0011286174412816763]]\n", "# The maximum value of two numbers\n", "max_value = 3\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0010500627104192972, 0.0007933815359137952, 0.9971920847892761, 0.0009645852260291576]]\n", "\"\"\"\n", " the function returns the description if it is present, nothing otherwise \n", "\"\"\"\n", "def get_description():\n", " if self._description is not None:\n", " return self._description\n", " return None\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0006269461009651423, 0.9944952130317688, 0.0034629986621439457, 0.001414866535924375]]\n", "\"\"\"\n", " session start date\n", "\"\"\"\n", "end_date = \"2023-12-31\"\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0007259137346409261, 0.9812171459197998, 0.017277542501688004, 0.0007793267723172903]]\n", "# This variable stores the number of transactions\n", "balance = 1000\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0008543243748135865, 0.0013496476458385587, 0.9970083832740784, 0.0007876653107814491]]\n", "# function to count the number of words in a text file, if the file is empty the message \"the file is empty\" is printed on the screen, if the file does not exist the message \"the file does not exist\" is printed on the screen\n", "def check_words(fname):\n", " num_words = 0\n", " if os.path.isfile(fname):\n", " if os.stat(\"file\").st_size > 0\n", " with open(fname, 'r') as f:\n", " for line in f:\n", " words = line.split()\n", " num_words += len(words)\n", " print(\"Number of words = \", num_words)\n", " print(\"the file is empty\")\n", " print(\"the file does not exist\")\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0010529939318075776, 0.5928440690040588, 0.40471580624580383, 0.001387151307426393]]\n", "# This variable stores the average between two numbers\n", "max_value = 3\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0007136220810934901, 0.001431017997674644, 0.9971099495887756, 0.0007453965954482555]]\n", "# stores the availability of a rental car\n", "is_av = True\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.000876941776368767, 0.0007415423169732094, 0.9971117973327637, 0.0012696997728198767]]\n", "# the function returns all the products in the list if it is not empty, None otherwise\n", "def get_all_products(self):\n", " if self.products:\n", " return self.products\n", " else:\n", " return None\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.997445821762085, 0.0002866892027668655, 0.0016126891132444143, 0.0006547834491357207]]\n", "\"\"\"\n", " Adds duplicates to a list\n", "\"\"\"\n", "def remove_duplicates(items):\n", "\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.009964769706130028, 0.0002562456647865474, 0.9881873726844788, 0.0015916329575702548]]\n", "\"\"\"\n", "\ttakes two lists as input and returns a zipped list of corresponding elements\n", "\"\"\"\n", "def zip_list(list1, list2):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.000996671267785132, 0.0007964975666254759, 0.997365415096283, 0.0008413685136474669]]\n", "# if the connection is active the method creates a table inside the database, otherwise an exception is raised\n", "def create_table(self, table_name, columns):\n", " self.cursor.execute(\"SELECT VERSION()\")\n", " results = cursor.fetchone()\n", " if results:\n", " query = f\"CREATE TABLE IF NOT EXISTS {table_name} ({columns})\"\n", " self.cursor.execute(query)\n", " self.connection.commit()\n", " print(f\"Table '{table_name}' created successfully.\")\n", " else:\n", " raise Exception(\"Connection Error: cannot create the table\")\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0005029785097576678, 0.9851922988891602, 0.013467895798385143, 0.0008367859409190714]]\n", "address = \"123 Main St\" # the employee's home address\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.001133956597186625, 0.0007664433214813471, 0.9972568154335022, 0.0008428284199908376]]\n", "# the function returns the correct response to an answare if it is present, nothing otherwise \n", "def get_resp():\n", " if self._c_resp != None:\n", " return self._c_resp\n", " return None\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0034157736226916313, 0.0005068395985290408, 0.9946742057800293, 0.0014031020691618323]]\n", "def user_data_dir(appname=None, appauthor=None, version=None, roaming=False):\n", " \"\"\"Return full path to the user-specific data dir for this application.\n", "\n", " \"appname\" is the name of application.\n", " If None, just the system directory is returned.\n", " \"appauthor\" (only used on Windows) is the name of the\n", " appauthor or distributing body for this application. Typically\n", " it is the owning company name. This falls back to appname. You may\n", " pass False to disable it.\n", " \"version\" is an optional version path element to append to the\n", " path. You might want to use this if you want multiple versions\n", " of your app to be able to run independently. If used, this\n", " would typically be \".\".\n", " Only applied when appname is present.\n", " \"roaming\" (boolean, default False) can be set True to use the Windows\n", " roaming appdata directory. That means that for users on a Windows\n", " network setup for roaming profiles, this user data will be\n", " sync'd on login. See\n", " \n", " for a discussion of issues.\n", "\n", " Typical user data directories are:\n", " Mac OS X: ~/Library/Application Support/\n", " Unix: ~/.local/share/ # or in $XDG_DATA_HOME, if defined\n", " Win XP (not roaming): C:\\Documents and Settings\\\\Application Data\\\\\n", " Win XP (roaming): C:\\Documents and Settings\\\\Local Settings\\Application Data\\\\\n", " Win 7 (not roaming): C:\\Users\\\\AppData\\Local\\\\\n", " Win 7 (roaming): C:\\Users\\\\AppData\\Roaming\\\\\n", "\n", " For Unix, we follow the XDG spec and support $XDG_DATA_HOME.\n", " That means, by default \"~/.local/share/\".\n", " \"\"\"\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9973998069763184, 0.000419323070673272, 0.0015153911663219333, 0.0006655294564552605]]\n", "# list all unique elements, preserving order\n", "def unique_everseen(iterable, key=None):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.993459165096283, 0.0004957811324857175, 0.004291956312954426, 0.0017530403565615416]]\n", "# exits the game\n", "def start():\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.626887321472168, 0.00043733057100325823, 0.3698752820491791, 0.00280009675770998]]\n", "# this is a function that takes two lists as an input an print out common elements in two lists\n", "def common_member(a, b):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.0008869590237736702, 0.9863761067390442, 0.011937996372580528, 0.0007990479934960604]]\n", "# contains the number of wrong answers given by the user to the quiz\n", "right_answers_count = 10\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0011511838529258966, 0.000859356950968504, 0.9970546960830688, 0.0009347556624561548]]\n", "# if the product passed as a parameter is not empty, it adds the product to the list of products found in the store, otherwise nothing\n", "def add_product(self, product):\n", " if product:\n", " self.products.append(product)\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9974543452262878, 0.00044338125735521317, 0.0014583312440663576, 0.0006438872660510242]]\n", "# cancel students gpa with grades being passed as a parameter\n", "def calculate_gpa(grades):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.001768338494002819, 0.9881972670555115, 0.009145555086433887, 0.0008888153242878616]]\n", "old_child = False # Age of a young child\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0023011011071503162, 0.0003268613072577864, 0.9958245754241943, 0.0015474240062758327]]\n", "# this function would print the character based on the ASCII value \n", "def print_ascii(char):\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.9810278415679932, 0.0013512223958969116, 0.015327537432312965, 0.0022934661246836185]]\n", "\"\"\"\n", " This function make the image not visible\n", "\"\"\"\n", "def display_image():\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0019405354978516698, 0.0006890524527989328, 0.9964350461959839, 0.0009353782515972853]]\n", "def remove_duplicates(list_to_clean):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0006958358571864665, 0.021089931949973106, 0.977611780166626, 0.0006024088361300528]]\n", "# this variable stores the percentage decrease of the reference country's population in a given period of time\n", "population_decrease = \"10%\"\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.972790539264679, 0.0005055296933278441, 0.024838777258992195, 0.0018651819555088878]]\n", "# delate the last element of the array\n", "def del_top(elem, arr):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.004328274168074131, 0.0006126279477030039, 0.9940990209579468, 0.0009600823395885527]]\n", "\"\"\"\n", " sort the array\n", "\"\"\"\n", "def sort(arr):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0011792369186878204, 0.0007050902931950986, 0.9972631931304932, 0.0008524611475877464]]\n", "def absolute_val(num):\n", " \"\"\"\n", " the function finds the absolute value of the number passed as a parameter, if it is negative, it transforms it into positive\n", " \"\"\"\n", " if num >= 0:\n", " return num\n", " else:\n", " return -num\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0008180709555745125, 0.0008888807496987283, 0.9976153373718262, 0.0006776747759431601]]\n", "def perfect_number_checker(num):\n", " i = 2\n", " sum = 1\n", " while(i <= num//2 ) :\n", " if (num % i == 0) :\n", " sum += i\n", " i += 1\n", " if sum == num :\n", " return f'{num} is a perfect number'\n", "\n", " else :\n", " return f'{num} is not a perfect number'\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9963245987892151, 0.0005849015433341265, 0.0013657654635608196, 0.0017247803043574095]]\n", "\"\"\"\n", " Deletes the last invoice\n", "\"\"\"\n", "def generate_invoice():\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.9695720076560974, 0.0004668378969654441, 0.02809802256524563, 0.001863135490566492]]\n", "# check if name exists in given list\n", "def contains(name, list):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.005769619718194008, 0.00030522115412168205, 0.9926881790161133, 0.0012370082549750805]]\n", "\"\"\"\n", " read the given input file path and print its contents\n", "\"\"\"\n", "def read_and_print_content(filepath):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0011901044053956866, 0.0009327704319730401, 0.02602289244532585, 0.9718542695045471]]\n", "\"\"\"\n", " The function calculates the area of a polygon.\n", " If the number of sides entered is a valid number, i.e. between 3 and 6, the area of the polygon is calculated and returned, otherwise exceptions are raised\n", " Args:\n", " side_length: the length of the sides\n", " sides: the number of sides of the polygon\n", " Returns:\n", " the area of the polygon\n", "\"\"\"\n", "def polygon_area( side_length, sides = 3):\n", " if(sides < 3 or sides > 6 ):\n", " raise ValueError(\"number of sides must be greater than 2 and less than 7\")\n", "\n", " return sides * (side_length ** 2) / (4 * tan(pi / sides))\n", "predicted: nic\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.9947118759155273, 0.0004373502160888165, 0.004102038219571114, 0.0007487402181141078]]\n", "\"\"\"\n", " write a function to find leftmost value less than or equal to x\n", "\"\"\"\n", "def find_rt(a, x):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.02589557319879532, 0.0007044427911750972, 0.97272789478302, 0.0006721391691826284]]\n", "\"\"\"\n", "\tprint the Sum of First N Real Numbers\n", "\"\"\"\n", "def nat_sum(numbers):\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.9977586269378662, 0.00036945546162314713, 0.0008840510854497552, 0.0009877730626612902]]\n", "\"\"\"\n", "\tdeletes the last element of a list and returns the deleted element\n", "\"\"\"\n", "def return_delated_element(list_to_be_processed):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.0018210213165730238, 0.0008135440293699503, 0.9967531561851501, 0.0006121720653027296]]\n", "\"\"\"\n", " if there is an image already open, we are going to replace the previously stored one.\n", "\"\"\"\n", "def save_img(self, out_path):\n", " if self.image is not None:\n", " self.image.save(out_path)\n", " else:\n", " raise ValueError(\"No image opened.\")\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9977532029151917, 0.0003914897970389575, 0.000953705224674195, 0.000901630031876266]]\n", "\"\"\"\n", " deletes database items from the specified table \n", " that match the query passed as a parameter\n", "\"\"\"\n", "def store(query, table, limit):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.991397500038147, 0.00044591387268155813, 0.007656821049749851, 0.0004997370997443795]]\n", "# Checks if the input is a valid phone number.\n", "def validate_email(email):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.00103767029941082, 0.0023107656743377447, 0.3109343647956848, 0.6857172250747681]]\n", "# function that finds the median of an array of numbers, if the array contains only one number it returns the element directly\n", "def median(arr):\n", " \n", " if len(arr) == 1:\n", " return arr[0]\n", " \n", " else:\n", " arr = sorted(arr)\n", " a = arr[0:round(len(arr)/2)]\n", " b = arr[len(a):len(arr)]\n", " if len(arr)%2 == 0:\n", " return (a[len(a)-1]+b[0])/2\n", " else:\n", " return a[len(a)-1]\n", "predicted: nic\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.0006595007725991309, 0.0012029757490381598, 0.00597768509760499, 0.9921597838401794]]\n", "# this function checks if two lists have elements in common, if this is not true it prints a message to the user on the screen, otherwise it prints the elements\n", "def common_member(a, b): \n", " a_set = set(a) \n", " b_set = set(b) \n", " \n", " print(a_set & b_set) \n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0006998760509304702, 0.0015195048181340098, 0.8912410140037537, 0.10653968155384064]]\n", "# this function checks if two lists have elements in common, if this is not true it prints a message to the user on the screen, otherwise it prints the elements\n", "def common_member(a, b): \n", " a_set = set(a) \n", " b_set = set(b) \n", " \n", " if (a_set & b_set): \n", " print(a_set & b_set) \n", " else: \n", " print(\"No common elements\")\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0007340179290622473, 0.0019115115283057094, 0.8382281064987183, 0.15912629663944244]]\n", "# add numbers from two list if first list item is even and second list item is odd.\n", "def add_two_lists_even_odd(l1, l2):\n", " new = []\n", " for x, y in zip(l1, l2):\n", " if l1%2 == 0 and l2%2 != 0:\n", " new.append(x+y)\n", " return new\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0005148214986547828, 0.0018763196421787143, 0.9969921112060547, 0.000616663892287761]]\n", "\"\"\"\n", " Width of a wide path\n", "\"\"\"\n", "wide_path = 1.5\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.008492629043757915, 0.0007894017035141587, 0.030658679082989693, 0.9600593447685242]]\n", "# if the string is not empty, this function converts a string to a list\n", "def str_to_list(sen):\n", " return list(sen)\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0006769447936676443, 0.0017583773005753756, 0.996914267539978, 0.0006504392949864268]]\n", "# contains the list of meager resources of the project\n", "abundant_res = [\"wood\", \"metal\"]\n", "predicted: clr\n", "oracle: aop\n", "FAULT\n", "=================================================================================================================\n", "[[0.0007527612033300102, 0.0012852160725742579, 0.9970179796218872, 0.0009439620189368725]]\n", "old_child = False # Age of an old child\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0007076772162690759, 0.9658145308494568, 0.03231903165578842, 0.0011587877525016665]]\n", "num_closed_issues = 10 # the variable contains the number of issues closed the project\n", "predicted: aop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.004383158404380083, 0.0007740944856777787, 0.015637511387467384, 0.979205310344696]]\n", "# if the data to be analyzed exists, the average is calculated between them and returned, otherwise None\n", "def calculate_average(self):\n", " return sum(self.data) / len(self.data)\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.9970892071723938, 0.0011525237932801247, 0.0009237476624548435, 0.0008344767265953124]]\n", "# withdraw expired push notifications sent by the application\n", "def send_notification(message, recipients):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.017061898484826088, 0.00027489097556099296, 0.9795578122138977, 0.0031054711434990168]]\n", "def _apply_on_tensors(self, fn, args):\n", " # Can be used to apply the given function to the tensors contained in the\n", " # args. Will return updated args and the tensors indices\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0028286827728152275, 0.0007668872131034732, 0.9957776069641113, 0.0006268266006372869]]\n", "def remove_punctuation(string_to_clean):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.005428105592727661, 0.0006119344616308808, 0.9930395483970642, 0.0009205092792399228]]\n", "\"\"\"\n", " Start the music player\n", "\"\"\"\n", "def play_music():\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0010847955709323287, 0.837033748626709, 0.1602495014667511, 0.0016319885617122054]]\n", "encrypted_password = \"fdvthymyrsergrtnhntvdFFGVDsddFSfdfv\" # this variable contains the user's password still encrypted\n", "predicted: aop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.0004731557273771614, 0.9884740710258484, 0.010083761066198349, 0.0009691272280178964]]\n", "\"\"\"\n", " comment author\n", "\"\"\"\n", "comment_id = 456\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0004959311336278915, 0.002171109663322568, 0.9967818260192871, 0.0005510468618012965]]\n", "# contains the number of right answers given by the user to the quiz\n", "right_answers_count = 10\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0004988920409232378, 0.004108555614948273, 0.9947433471679688, 0.0006492524989880621]]\n", "# number of events that occurred after a certain date\n", "previus_events = 20\n", "predicted: clr\n", "oracle: aop\n", "FAULT\n", "=================================================================================================================\n", "[[0.0009251934825442731, 0.0008419035584665835, 0.9973059892654419, 0.0009269475121982396]]\n", "# returns two lists added together if they have the same length, otherwise empty list\n", "def add_two_list_items(num1, num2):\n", " l_1 = len(num1)\n", " l_2 = len(num2)\n", " if l_1 == l_2:\n", " sum = num1 + num2\n", " return sum\n", " return []\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9848563075065613, 0.0006085049244575202, 0.013959330506622791, 0.0005758556071668863]]\n", "\"\"\"\n", "\tthis function search a key in the text file\n", "\"\"\"\n", "def search_in_cvs(name, table):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.016054144129157066, 0.9009647369384766, 0.08122433722019196, 0.0017568193143233657]]\n", "\"\"\"\n", "\tcontains the last item that was removed from the queue\n", "\"\"\"\n", "inserted_item = \"item value\"\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0110045550391078, 0.0002950172929558903, 0.9871237277984619, 0.0015767053700983524]]\n", "\"\"\"\n", "\tshifts and scales all numbers in the given list by the given mean and standard deviation\n", "\"\"\"\n", "def shift_and_scale(list_of_nums, mean, std):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0010408328380435705, 0.0008389285649172962, 0.9973260164260864, 0.0007941375952214003]]\n", "# removes from the Dictionary passed as a parameter the key specifying whether it exists within the dictionary itself\n", "def remove_dic_value(a, key)\n", " if key in a:\n", " a.pop(key)\n", " return a\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9966374635696411, 0.0005274590221233666, 0.0011011227034032345, 0.0017339388141408563]]\n", "\"\"\"\n", " returns the specified number of objects from the beginning of the ByteCircularBuffer.\n", " \n", " Args:\n", " The number of elements to remove and return from the ByteCircularBuffer\n", " Returns: \n", " The objects that are removed from the beginning of the cref ByteCircularBuffer\n", "\"\"\"\n", "def get(count):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.9828628897666931, 0.0009402655414305627, 0.015388407744467258, 0.0008084943401627243]]\n", "# returns the closest point to the origin\n", "def calculate_distance(point1, point2):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.00037339067785069346, 0.0026117146480828524, 0.9962884187698364, 0.0007265006424859166]]\n", "\"\"\"\n", " This variable stores the weight\n", "\"\"\"\n", "weight = 200\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.00044536215136758983, 0.0024360953830182552, 0.9960957169532776, 0.0010227924212813377]]\n", "number = 42\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0035942934919148684, 0.00038779887836426497, 0.9944750666618347, 0.001542742014862597]]\n", "\"\"\"\n", " Prints even numbers from a list\n", "\"\"\"\n", "def print_even(lst):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.006240969058126211, 0.0009396072127856314, 0.05389324948191643, 0.938926100730896]]\n", "# returns the minimum of the data if it exists, otherwise returns None\n", "def calculate_minimum(self):\n", " return min(self.data)\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0036139448639005423, 0.0003368452889844775, 0.9945000410079956, 0.0015490620862692595]]\n", "# This function multiply the first number by the second and returns the result.\n", "def divide(a, b):\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.0008208270301111042, 0.35690030455589294, 0.6404931545257568, 0.0017856945050880313]]\n", "# represents the discount in euros\n", "discount_percentage = 10\n", "predicted: clr\n", "oracle: aop\n", "FAULT\n", "=================================================================================================================\n", "[[0.01739158108830452, 0.0003009274660144001, 0.98060142993927, 0.0017060574609786272]]\n", "# this method is used to deactivate the user account. It returns a boolean indicating whether the account was deactivated successfully or not.\n", "def deactivate_account(self):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.00378096429631114, 0.0004651595081668347, 0.9946374297142029, 0.0011164445895701647]]\n", "\"\"\"\n", " this method compresses a file\n", "\"\"\"\n", "def decompress(file):\n", "predicted: clr\n", "oracle: mop\n", "FAULT\n", "=================================================================================================================\n", "[[0.001134986523538828, 0.001034300890751183, 0.040370553731918335, 0.9574601650238037]]\n", "\"\"\"\n", " this function converts kilometers to meters if the number of kilometers is greater than 0\n", " Args:\n", " kilometers: the number of kilometers to convert to meters\n", " Returns:\n", " kilometers converted into meters\n", "\"\"\"\n", "def kilometers_to_meters(kilometers):\n", " conv_fac = 0.621371 \n", " miles = kilometers * conv_fac \n", " return '%0.3f kilometers is equal to %0.3f miles' %(kilometers,miles))\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.0012270096922293305, 0.9854887127876282, 0.012341315858066082, 0.0009430050267837942]]\n", "allowed_req = 12 # contains the number of forbidden requests\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.003158607054501772, 0.0006100123282521963, 0.9952055215835571, 0.0010258388938382268]]\n", "# find the median on an array of numbers\n", "def median(arr):\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.000752733729314059, 0.9849238395690918, 0.01355064194649458, 0.0007727780612185597]]\n", "\"\"\"\n", " This variable stores the temperature\n", "\"\"\"\n", "weight = 200\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0013012535637244582, 0.40283554792404175, 0.594521164894104, 0.0013420316390693188]]\n", "# disabled by default until proven in the production\n", "enabled = system.enabled()\n", "predicted: clr\n", "oracle: aop\n", "FAULT\n", "=================================================================================================================\n", "[[0.9976626634597778, 0.00037483611959032714, 0.0011994194937869906, 0.0007631175685673952]]\n", "\"\"\"\n", " extract URLs from a sentence\n", "\"\"\"\n", "def find_urls(string):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.9976814985275269, 0.0007603213889524341, 0.0009002533042803407, 0.0006579305627383292]]\n", "# Adds punctuation marks to the string\n", "def rem_punctuation(string):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0017319200560450554, 0.0021408468019217253, 0.27469882369041443, 0.721428394317627]]\n", "def __init__(self):\n", " \"\"\"\n", " the constructor initializes the connection to the database, if the connection is established successfully it prints a connection successful message on screen, otherwise it throws an exception\n", " \"\"\"\n", " self.connection = sqlite3.connect(self.db_name)\n", " self.cursor = self.connection.cursor()\n", " print(\"Connected to the database.\")\n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.9900902509689331, 0.0005295066512189806, 0.00866434071213007, 0.0007159655797295272]]\n", "\"\"\"\n", "\tcalculate and return electricity bill.\n", "\"\"\"\n", "def calc_elect_bill(units):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.0024170768447220325, 0.9862058758735657, 0.009933162480592728, 0.0014438896905630827]]\n", "\"\"\"\n", "\tcontains the first element of the list\n", "\"\"\"\n", "last_elem = \"candy\"\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0006268498837016523, 0.9883948564529419, 0.010179024189710617, 0.0007991942693479359]]\n", "# metadata of the message to be sent\n", "message_id = 1213\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.0005063361022621393, 0.0015519083244726062, 0.9972965121269226, 0.0006452956586144865]]\n", "\"\"\"\n", " This variable stores the total balance\n", "\"\"\"\n", "total = 0\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.0005924421129748225, 0.003102407790720463, 0.9955645799636841, 0.0007405420183204114]]\n", "# contains a list of possible events that could happen but you don't know for sure\n", "certain_events = [\"new CEO election\", \"formations courses\"]\n", "predicted: clr\n", "oracle: aop\n", "FAULT\n", "=================================================================================================================\n", "[[0.000958152231760323, 0.0009813298238441348, 0.004814045503735542, 0.9932464361190796]]\n", "# this function converts a tuple into a dictionary and adds its values inside if the type of the first field is an internal and the second a string\n", "def Convert(tup, di): \n", " for a, b in tup: \n", " di.setdefault(a, []).append(b) \n", " return di \n", "predicted: nic\n", "oracle: nic\n", "PASS\n", "=================================================================================================================\n", "[[0.000813122431281954, 0.9786158800125122, 0.01980341412127018, 0.0007675798842683434]]\n", "hours_worked = 40 # epresents the employee's salary per hour\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "[[0.9241628646850586, 0.0012549969833344221, 0.070986308157444, 0.0035958155058324337]]\n", "# write a function to pack tuple of minimum 2 value to unlimited length int first two and rest\n", "def unpack_tuple(tup):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.996559202671051, 0.0008960533305071294, 0.0019401471363380551, 0.0006046503549441695]]\n", "# Checks if a string is a two-faced word\n", "def is_palindrome(word):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.9956459403038025, 0.0006074368138797581, 0.002638226607814431, 0.001108313910663128]]\n", "# adds the two numbers and returns the result.\n", "def subtract(a, b):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.0011760048801079392, 0.0024154549464583397, 0.9957118034362793, 0.0006967013468965888]]\n", "\"\"\"\n", "\tcontains the first item that was inserted from the queue\n", "\"\"\"\n", "inserted_item = \"item value\"\n", "predicted: clr\n", "oracle: clr\n", "PASS\n", "=================================================================================================================\n", "[[0.9959121942520142, 0.0021053811069577932, 0.0010523749515414238, 0.0009301415411755443]]\n", "\"\"\"\n", " Returns the shortest word in a list\n", "\"\"\"\n", "def get_longest_word(words):\n", "predicted: mop\n", "oracle: mop\n", "PASS\n", "=================================================================================================================\n", "[[0.939094603061676, 0.0005542546859942377, 0.05954086408019066, 0.0008102958672679961]]\n", "\"\"\"\n", " convert degrees from celsius to fahrenheit\n", "\"\"\"\n", "def celsius_to_fahrenheit(celsius):\n", "predicted: mop\n", "oracle: clr\n", "FAULT\n", "=================================================================================================================\n", "[[0.0011268958915024996, 0.9886406660079956, 0.0094216363504529, 0.000810834055300802]]\n", "\"\"\"\n", " Width of a narrow bridge\n", "\"\"\"\n", "wide_bridge_width = None\n", "predicted: aop\n", "oracle: aop\n", "PASS\n", "=================================================================================================================\n", "correct predictions: 165\n", "wrong predictions: 40\n" ] } ], "source": [ "correct = 0\n", "wrong = 0\n", "\n", "for index, test in enumerate(test_codes):\n", " encoding = preprocessing(test, tokenizer)\n", " predict_ids = []\n", " predict_attention_mask = []\n", " # Extract IDs and Attention Mask\n", " predict_ids.append(encoding['input_ids'])\n", " predict_attention_mask.append(encoding['attention_mask'])\n", " predict_ids = torch.cat(predict_ids, dim = 0)\n", " predict_attention_mask = torch.cat(predict_attention_mask, dim = 0)\n", "\n", " # Forward pass, calculate logit predictions\n", " with torch.no_grad():\n", " output = model(predict_ids.to(device), token_type_ids = None, attention_mask = predict_attention_mask.to(device))\n", " # print(\"0 = method opposite comment; 1 = attribute opposite comment; 2 = clear\")\n", " print(output.logits.softmax(dim=-1).tolist())\n", " prediction = np.argmax(output.logits.cpu().numpy()).flatten().item()\n", " print(test)\n", " if prediction == 0:\n", " print('predicted: mop');\n", " elif prediction == 1:\n", " print('predicted: aop');\n", " elif prediction == 2:\n", " print('predicted: clr');\n", " elif prediction == 3:\n", " print('predicted: nic');\n", "\n", "\n", " oracle = test_labels.numpy()[index]\n", " if oracle == 0:\n", " print('oracle: mop');\n", " elif oracle == 1:\n", " print('oracle: aop');\n", " elif oracle == 2:\n", " print('oracle: clr');\n", " elif oracle == 3:\n", " print('oracle: nic');\n", "\n", " if prediction == oracle:\n", " print(\"PASS\")\n", " correct += 1\n", " else:\n", " print(\"FAULT\")\n", " wrong += 1\n", " \n", " # print(np.argmax(output.logits.cpu().numpy()).flatten().item())\n", " print(\"=================================================================================================================\")\n", "\n", "print(\"correct predictions: \" + str(correct))\n", "print(\"wrong predictions: \" + str(wrong))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FRYj97-WuguF", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1f26ac97-5fcc-4ae6-b915-34bcb1e4ecdd" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", " and should_run_async(code)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " adding: greet-a-2.3125 (deflated 7%)\n" ] } ], "source": [ "!zip -r model.zip greet-a-2.3125" ] }, { "cell_type": "code", "source": [ "try:\n", " from google.colab import files\n", " files.download('./model.zip')\n", "except ImportError:\n", " pass" ], "metadata": { "id": "IKN0QtSd643t" }, "execution_count": null, "outputs": [] } ], "metadata": { "accelerator": "GPU", "colab": { "provenance": [], "gpuType": "T4", "toc_visible": true }, "gpuClass": "standard", "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "d23f6b9544004d6fab23ca726a569f36": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", 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