diff --git "a/.ipynb_checkpoints/Untitled-checkpoint.ipynb" "b/.ipynb_checkpoints/Untitled-checkpoint.ipynb" deleted file mode 100644--- "a/.ipynb_checkpoints/Untitled-checkpoint.ipynb" +++ /dev/null @@ -1,2149 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 48, - "id": "db3454c3-ea99-49f3-8b2a-68103e7bb139", - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "from gensim.models.keyedvectors import KeyedVectors\n", - "from transformers import pipeline\n", - "import pickle\n", - "import numpy as np\n", - "import pandas as pd\n", - "import plotly.express as px" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d9dc09a3-1dbf-4fb5-8816-135fbeaf08fc", - "metadata": {}, - "outputs": [], - "source": [ - "w2v = KeyedVectors.load('models/word2vec')\n", - "w2v_vocab = set(sorted(w2v.index_to_key))\n", - "model = pickle.load(open('models/w2v_ovr_svc.sav', 'rb'))\n", - "classifier = pipeline(\"zero-shot-classification\",\n", - " model=\"facebook/bart-large-mnli\", framework='pt'\n", - " )\n", - "\n", - "labels = [\n", - " 'communication', 'waiting time',\n", - " 'information', 'user interface',\n", - " 'facilities', 'location', 'price'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "5008335c-d616-4618-bdd8-4d4754bd81b3", - "metadata": {}, - "outputs": [], - "source": [ - "def get_single_sentiment_label_facebook(list_of_sent_dicts):\n", - " return {list_of_sent_dicts['labels'][i]: round(list_of_sent_dicts['scores'][i], 2) for i in range(2)}" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "803a6715-67c4-40a1-8092-e730026a2d69", - "metadata": {}, - "outputs": [], - "source": [ - "def get_single_prediction(text):\n", - " \n", - " # manipulate data into a format that we pass to our model\n", - " text = text.lower() #lower case\n", - " text = re.sub('[^0-9a-zA-Z\\s]', '', text) #remove special char, punctuation\n", - "\n", - " # Remove OOV words\n", - " text = ' '.join([i for i in text.split() if i in w2v_vocab])\n", - " \n", - " # Vectorise text and store in new dataframe. Sentence vector = average of word vectors\n", - " text_vectors = np.mean([w2v[i] for i in text.split()], axis=0)\n", - "\n", - " # Make predictions\n", - " results = model.predict_proba(text_vectors.reshape(1,300)).squeeze().round(2)\n", - " pred_prob = pd.DataFrame({'topic': labels, 'probability': results}).sort_values('probability', ascending=True)\n", - " \n", - " # Get sentiment\n", - " sentiment_results = classifier(text, \n", - " candidate_labels=['positive', 'negative'], \n", - " hypothesis_template='The sentiment of this is {}')\n", - " sentiment_prob = pd.DataFrame({'sentiment': sentiment_results['labels'], 'probability': sentiment_results['scores']})\n", - " \n", - " return (pred_prob, sentiment_prob)" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "5499ec1b-b50c-484c-b2c6-4df2a2326791", - "metadata": {}, - "outputs": [], - "source": [ - "a,b = get_single_prediction('the price was very good')" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "4b7a3c76-54b8-4033-ab50-0dedc18cc249", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"title": { - "text": "Sentiment Probability" - } - } - }, - "image/png": 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", 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