news_category_similar_news_prediction.py

import pandas as pd
import numpy as np
import tensorflow as tf 
from config import (DISTILBERT_TOKENIZER_N_TOKENS, 
                    NEWS_CATEGORY_CLASSIFIER_N_CLASSES, 
                    CLASSIFIER_THRESHOLD, NEWS_RETENTION_SECONDS)

from logger import get_logger
from find_similar_news import find_similar_news

logger = get_logger()

##########################

from dateutil import parser
def correct_date(x):
    if (not isinstance(x, str)) or (str(x).find(":") == -1):
        logger.warning(f'correct_date() error: {x} is not the right date format')
        return "2020-11-07 00:36:44+05:30"
    return x

def date_time_parser(dt):
    """
    Computes the minutes elapsed since published time.
    :param dt: date
    :return: int, minutes elapsed.
    """
    try:
        return int(np.round((dt.now(dt.tz) - dt).total_seconds() / 60, 0))
    except:
        logger.warning(f'date_time_parser() error: {dt} is not the right date format')
        return 100000

def delete_outdated_news(final_df: pd.DataFrame):
    logger.warning("Entering delete_outdated_news()")
    final_df = final_df.copy()
    final_df["parsed_date_1"] = [correct_date(date_) for date_ in final_df['parsed_date']]
    final_df["parsed_date_1"] = [parser.parse(date_) for date_ in final_df['parsed_date_1']]
    final_df["elapsed_time"] =  [date_time_parser(date_) for date_ in final_df['parsed_date_1']]
    final_df = final_df.loc[final_df["elapsed_time"] <= NEWS_RETENTION_SECONDS, :].copy()
    final_df.drop(columns=['elapsed_time', 'parsed_date_1'], inplace=True)
    final_df.reset_index(drop=True, inplace=True)
    logger.warning("Exiting delete_outdated_news()")
    return final_df



############################################


def parse_prediction(tflite_pred, label_encoder):
    tflite_pred_argmax = np.argmax(tflite_pred, axis=1)
    tflite_pred_label = label_encoder.inverse_transform(tflite_pred_argmax)
    tflite_pred_prob = np.max(tflite_pred, axis=1)
    return tflite_pred_label, tflite_pred_prob
    

def inference(text, interpreter, label_encoder, tokenizer):
    logger.warning('Entering inference()')
    batch_size = len(text)
    logger.warning(f'Samples to predict: {batch_size}')
    if text != "":
        tokens = tokenizer(text, max_length=DISTILBERT_TOKENIZER_N_TOKENS, padding="max_length", truncation=True, return_tensors="tf")
        # tflite model inference  
        interpreter.allocate_tensors()
        input_details = interpreter.get_input_details()
        output_details = interpreter.get_output_details()[0]
        attention_mask, input_ids = tokens['attention_mask'], tokens['input_ids']
        interpreter.resize_tensor_input(input_details[0]['index'],[batch_size, DISTILBERT_TOKENIZER_N_TOKENS])
        interpreter.resize_tensor_input(input_details[1]['index'],[batch_size, DISTILBERT_TOKENIZER_N_TOKENS])
        interpreter.resize_tensor_input(output_details['index'],[batch_size, NEWS_CATEGORY_CLASSIFIER_N_CLASSES])
        interpreter.allocate_tensors()
        interpreter.set_tensor(input_details[0]["index"], attention_mask)
        interpreter.set_tensor(input_details[1]["index"], input_ids)
        interpreter.invoke()
        tflite_pred = interpreter.get_tensor(output_details["index"])
        tflite_pred = parse_prediction(tflite_pred, label_encoder)
    logger.warning('Exiting inference()')
    return tflite_pred

def cols_check(new_cols, old_cols):
    return all([new_col==old_col for new_col, old_col in zip(new_cols, old_cols)])

    
def predict_news_category_similar_news(old_news: pd.DataFrame, new_news: pd.DataFrame, interpreter, label_encoder, tokenizer,
                         collection, vectorizer, sent_model, ce_model):
    try:
        db_updation_required = 1
        logger.warning('Entering predict_news_category()')
        logger.warning(f'old news: {old_news}\nnew_news: {new_news}')
        if not isinstance(new_news, pd.DataFrame):
            raise Exception('No New News Found')
        else:
            # new_news = new_news.copy()
            logger.warning(f'{len(new_news)} new news items found before deleting outdated news')
            new_news = delete_outdated_news(new_news)
            logger.warning(f'{len(new_news)} new news items found after deleting outdated news')
            logger.warning(f'new news columns: {[*new_news.columns]}')
            if len(new_news) <= 1:
                new_news = None

        if not isinstance(new_news, pd.DataFrame):
            raise Exception('No New News Found after deleting outdated news')
            
        
        if isinstance(old_news, pd.DataFrame):
            # old_news = old_news.copy()
            logger.warning(f'{len(old_news)} old news items found before deleting outdated news')
            old_news = delete_outdated_news(old_news)
            logger.warning(f'{len(old_news)} old news items found after deleting outdated news')
            logger.warning(f'old news columns: {[*old_news.columns]}')
            
            old_news.drop(columns='_id', inplace=True)
            logger.warning('Dropped _id column from old news data frame.')
            if len(old_news) <= 1:
                old_news = None
        else:
            logger.warning('No old news is found')
            old_news = new_news.copy()

        if not isinstance(old_news, pd.DataFrame):
            logger.warning('No old news is found after deleting outdate news')
            old_news = new_news.copy()
      
        if 'category' not in [*old_news.columns]:
            logger.warning('No prior predictions found in old news')
            if not cols_check([*new_news.columns], [*old_news.columns]):
                raise Exception("New and old cols don't match")
            final_df = pd.concat([old_news, new_news], axis=0, ignore_index=True)
            final_df.drop_duplicates(subset='url', keep='first', inplace=True)

            
            headlines = [*final_df['title'].fillna("").str.strip()]
            descriptions = [*final_df['description'].fillna("").str.strip()]
            # label, prob = inference(headlines, interpreter, label_encoder, tokenizer)
            headlines_desc = [h if (h == d) else f"{h}. {d}" for h, d in zip(headlines, descriptions)]
            label, prob = inference(headlines_desc, interpreter, label_encoder, tokenizer)

            
            sent_embs = vectorizer.vectorize_(headlines, sent_model)
            sim_news = [find_similar_news(text, search_vec, collection, vectorizer, sent_model, ce_model) for search_vec, text in zip(sent_embs, headlines)]
            final_df['category'] = label
            final_df['pred_proba'] = prob
            final_df['similar_news'] = sim_news
            final_df.reset_index(drop=True, inplace=True)
            final_df.loc[final_df['pred_proba']<CLASSIFIER_THRESHOLD, 'category'] = 'NATION'
            final_df.loc[(final_df['title'].str.contains('Pakistan')) & (final_df['category'] == 'NATION'), 'category'] = 'WORLD'
            logger.warning('Updated category of articles having Pakistan in title and category=NATION to WORLD')
            final_df.loc[(final_df['title'].str.contains('Zodiac Sign', case=False)) | (final_df['title'].str.contains('Horoscope', case=False)), 'category'] = 'SCIENCE'
            logger.warning('Updated category of articles having Zodiac Sign in title to SCIENCE')
        else:
            logger.warning('Prior predictions found in old news')
            if not cols_check([*new_news.columns], [*old_news.columns][:-3]):
                raise Exception("New and old cols don't match")
            old_urls = [*old_news['url']]
            new_news = new_news.loc[new_news['url'].isin(old_urls) == False, :]
            if len(new_news) > 0:
                

                headlines = [*new_news['title'].fillna("").str.strip()]
                descriptions = [*new_news['description'].fillna("").str.strip()]
                # label, prob = inference(headlines, interpreter, label_encoder, tokenizer)
                headlines_desc = [h if (h == d) else f"{h}. {d}" for h, d in zip(headlines, descriptions)]
                label, prob = inference(headlines_desc, interpreter, label_encoder, tokenizer)

            
                sent_embs = vectorizer.vectorize_(headlines, sent_model)
                sim_news = [find_similar_news(text, search_vec, collection, vectorizer, sent_model, ce_model) for search_vec, text in zip(sent_embs, headlines)]
                new_news['category'] = label
                new_news['pred_proba'] = prob
                new_news['similar_news'] = sim_news
                final_df = pd.concat([old_news, new_news], axis=0, ignore_index=True)
                final_df.drop_duplicates(subset='url', keep='first', inplace=True)
                final_df.reset_index(drop=True, inplace=True)
                final_df.loc[final_df['pred_proba']<CLASSIFIER_THRESHOLD, 'category'] = 'NATION'
                final_df.loc[(final_df['title'].str.contains('Pakistan')) & (final_df['category'] == 'NATION'), 'category'] = 'WORLD'
                logger.warning('Updated category of articles having Pakistan in title and category=NATION to WORLD')
                final_df.loc[(final_df['title'].str.contains('Zodiac Sign', case=False)) | (final_df['title'].str.contains('Horoscope', case=False)), 'category'] = 'SCIENCE'
                logger.warning('Updated category of articles having Zodiac Sign in title to SCIENCE')
            else:
                logger.warning('INFO: Old & New Articles are the same. There is no requirement of updating them in the database. Database is not updated.')
                db_updation_required = 0
                final_df = old_news.copy()
                
        
        if len(final_df) == 0:
            final_df = None
        
        logger.warning('Exiting predict_news_category()')
    except Exception as e:
        logger.warning(f'Unexcpected error in predict_news_category()\n{e}')
        final_df = None
        db_updation_required = 0
    return final_df, db_updation_required