diff --git a/domains/sentiment/Step_by_step.ipynb b/domains/sentiment/Step_by_step.ipynb new file mode 100644 index 0000000..b0803d3 --- /dev/null +++ b/domains/sentiment/Step_by_step.ipynb @@ -0,0 +1,7958 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this walkthrough, we will develop a neural Sentiment Analysis model for Norwegian without access to labelled data. The first thing we will do is to read in our data and get it into Spacy DocBins:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import spacy\n", + "from spacy.tokens import DocBin\n", + "import pandas as pd\n", + "\n", + "nlp = spacy.load(\"nb_core_news_md\")\n", + "\n", + "train_doc_bin = DocBin(store_user_data=True)\n", + "dev_doc_bin = DocBin(store_user_data=True)\n", + "test_doc_bin = DocBin(store_user_data=True)\n", + "\n", + "train = pd.read_csv(\"../../data/sentiment/norec_sentence/train.txt\", delimiter=\"\\t\", header=None)\n", + "dev = pd.read_csv(\"../../data/sentiment/norec_sentence/dev.txt\", delimiter=\"\\t\", header=None)\n", + "test = pd.read_csv(\"../../data/sentiment/norec_sentence/test.txt\", delimiter=\"\\t\", header=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We load the data as a pandas dataframe, where the first column is the sentence index, the second is the polarity (0=Negative, 1=Neutral, 2=Positive), and the third is the pretokenized sentence, as a string. We then create a Spacy DocBin object for train, dev, and test splits." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1\n", + "0 1 Stor og bred Seagal\n", + "1 1 Steven Seagal er blitt like stor som Travolta ...\n", + "2 1 I denne filmen handler det om en seriemorder m...\n", + "3 1 Dermed er genrens patologi-imperativ ivaretatt .\n", + "4 0 Verre er det med slagsmålene .\n", + "5 0 Klipperen har overtatt Seagals martial art , o...\n", + "6 1 Men noen fikse ting :\n", + "7 1 Hettemannen ser ut som David Beckham etter en ...\n", + "8 1 Seagal får en Dirty Harry-pakke :\n", + "9 2 Ei finslig dame skal bli med på kjøret og obse...\n" + ] + } + ], + "source": [ + "print(train.head(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "for sid, (label, sent) in train.iterrows():\n", + " doc = nlp(sent)\n", + " doc.user_data[\"gold\"] = label\n", + " train_doc_bin.add(doc)\n", + "train_doc_bin.to_disk(\"../../data/sentiment/norec_sentence/train.docbin\")\n", + "\n", + "for sid, (label, sent) in dev.iterrows():\n", + " doc = nlp(sent)\n", + " doc.user_data[\"gold\"] = label\n", + " dev_doc_bin.add(doc)\n", + "dev_doc_bin.to_disk(\"../../data/sentiment/norec_sentence/dev.docbin\")\n", + "\n", + "for sid, (label, sent) in test.iterrows():\n", + " doc = nlp(sent)\n", + " doc.user_data[\"gold\"] = label\n", + " test_doc_bin.add(doc)\n", + "test_doc_bin.to_disk(\"../../data/sentiment/norec_sentence/test.docbin\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will begin to build up our labelling functions. We will start by training a document-level BOW model on the NoReC documents. These are given star ratings from 1-6, so the labelling function will have to map these to negative, neutral, and positive." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting model on ../../data/sentiment/conllu.tar.gz\n", + "Doc-level F1: 0.313\n", + "Saving vectorizer and model to ../../data/sentiment/models/doc\n" + ] + } + ], + "source": [ + "import sys\n", + "sys.path.insert(0, '../..')\n", + "import skweak\n", + "from sklearn.metrics import f1_score\n", + "from sentiment_models import DocBOWAnnotator\n", + "\n", + "dann = DocBOWAnnotator(\"doc_norec\",\n", + " model_path=\"../../data/sentiment/models/doc\",\n", + " doclevel_data=\"../../data/sentiment/conllu.tar.gz\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will train a multi-lingual BERT model on the Stanford Sentiment Treebank." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of the model checkpoint at bert-base-multilingual-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n", + "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", + "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-multilingual-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n", + " 0%| | 0/534 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBertTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bert-base-multilingual-uncased\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m 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\"../../data/sentiment/models/mbert-sst\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can construct a full annotator with all annotators described above, and then run it on the NoReC sentence-level dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading lexicon functions\n", + "Loading learned sentiment model functions\n", + "Loaded model from ../../data/sentiment/models/doc\n", + "Loaded nlptown/bert-base-multilingual-uncased-sentiment\n", + "Loaded mBERT from ../../data/sentiment/models/mbert-sst\n", + "Number of processed documents: 1\n", + "Number of processed documents: 2\n", + "Number of processed documents: 3\n", + "Number of processed documents: 4\n", + "Number of processed documents: 5\n", + "Number of processed documents: 6\n", + "Number of processed documents: 7\n", + "Number of processed documents: 8\n", + "Number of 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FullSentimentAnnotator\n", + "\n", + "ann = FullSentimentAnnotator()\n", + "ann.add_all()\n", + "\n", + "ann.annotate_docbin(\"../../data/sentiment/norec_sentence/train.docbin\",\n", + " \"../../data/sentiment/norec_sentence/train_pred.docbin\")\n", + "\n", + "ann.annotate_docbin(\"../../data/sentiment/norec_sentence/dev.docbin\",\n", + " \"../../data/sentiment/norec_sentence/dev_pred.docbin\")\n", + "\n", + "ann.annotate_docbin(\"../../data/sentiment/norec_sentence/test_pred.docbin\",\n", + " \"../../data/sentiment/norec_sentence/test_pred.docbin\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the HMM model from skweak to aggregate all the predictions from the weak labelling functions." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'IS_ALPHA'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0munified_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mskweak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHMM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"hmm\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence_labelling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0munified_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../../data/sentiment/norec_sentence/train_pred.docbin\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m unified_model.annotate_docbin(\"../../data/sentiment/norec_sentence/train_pred.docbin\",\n\u001b[1;32m 4\u001b[0m \"../../data/sentiment/norec_sentence/train_pred.docbin\")\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Exps/skweak/skweak/aggregation.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, docbin_file, cutoff, n_iter, tol, cutoff_for_init)\u001b[0m\n\u001b[1;32m 330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0;31m# And add the counts from majority voter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_mv_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[0;31m# Finally, we postprocess the counts and get probabilities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Exps/skweak/skweak/aggregation.py\u001b[0m in \u001b[0;36m_add_mv_counts\u001b[0;34m(self, docs)\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;31m# We extract the observations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 500\u001b[0;31m \u001b[0mobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_observation_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0;31m# And aggregate the results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Exps/skweak/skweak/aggregation.py\u001b[0m in \u001b[0;36mget_observation_df\u001b[0;34m(self, doc)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mspan\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspans\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0mspan_index\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msources\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msorted_unique_spans\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'IS_ALPHA'" + ] + } + ], + "source": [ + "unified_model = skweak.aggregation.HMM(\"hmm\", [0, 1, 2], sequence_labelling=False)\n", + "unified_model.fit(\"../../data/sentiment/norec_sentence/train_pred.docbin\")\n", + "unified_model.annotate_docbin(\"../../data/sentiment/norec_sentence/train_pred.docbin\",\n", + " \"../../data/sentiment/norec_sentence/train_pred.docbin\")\n", + "\n", + "unified_model.annotate_docbin(\"../../data/sentiment/norec_sentence/dev_pred.docbin\",\n", + " \"../../data/sentiment/norec_sentence/dev_pred.docbin\")\n", + "\n", + "unified_model.annotate_docbin(\"../../data/sentiment/norec_sentence/test_pred.docbin\",\n", + " \"../../data/sentiment/norec_sentence/test_pred.docbin\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also compare with a majority voting aggregator, which will highlight the benefits of skweak." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mv = skweak.aggregation.MajorityVoter(\"mv\", [0, 1, 2], sequence_labelling=False) #type: ignore\n", + "mv.annotate_docbin(\"../../data/sentiment/norec_sentence/test_pred.docbin\",\n", + " \"../../data/sentiment/norec_sentence/test_pred.docbin\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can evaluate everything" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pred_docs = list(utils.docbin_reader(\"../../data/sentiment/norec_sentence/test_pred.docbin\"))\n", + "gold = [d.user_data[\"gold\"] for d in pred_docs]\n", + "\n", + "# Evaluate the weak labelling approaches themselves\n", + "for lexicon in pred_docs[0].user_data[\"spans\"].keys():\n", + " pred = []\n", + " for d in pred_docs:\n", + " for span in d.spans[lexicon]:\n", + " pred.append(span.label_)\n", + "\n", + " lex_f1 = f1_score(gold, pred, average=\"macro\")\n", + " print(\"{0}:\\t{1:.3f}\".format(lexicon, lex_f1))\n", + " \n", + "for aggregator in [\"mv\", \"hmm\"]:\n", + " pred = []\n", + " for d in pred_docs:\n", + " for span in d.spans[aggregator]:\n", + " pred.append(span.label_)\n", + " agg_f1 = f1_score(gold, pred, average=\"macro\")\n", + " print(\"{0}:\\t{1:.3f}\".format(aggregator, agg_f1))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "myvenv", + "language": "python", + "name": "myvenv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}