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import pandas as pd |
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import numpy as np |
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import tensorflow as tf |
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def parse_prediction(tflite_pred, label_encoder): |
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tflite_pred_argmax = np.argmax(tflite_pred, axis=1) |
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tflite_pred_label = label_encoder.inverse_transform(tflite_pred_argmax) |
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tflite_pred_prob = np.max(tflite_pred, axis=1) |
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return tflite_pred_label, tflite_pred_prob |
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def inference(text, interpreter, label_encoder, tokenizer): |
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batch_size = len(text) |
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MAX_LEN = 80 |
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N_CLASSES = 8 |
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if text != "": |
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tokens = tokenizer(text, max_length=MAX_LEN, padding="max_length", truncation=True, return_tensors="tf") |
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interpreter.allocate_tensors() |
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input_details = interpreter.get_input_details() |
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output_details = interpreter.get_output_details()[0] |
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attention_mask, input_ids = tokens['attention_mask'], tokens['input_ids'] |
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interpreter.resize_tensor_input(input_details[0]['index'],[batch_size, MAX_LEN]) |
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interpreter.resize_tensor_input(input_details[1]['index'],[batch_size, MAX_LEN]) |
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interpreter.resize_tensor_input(output_details['index'],[batch_size, N_CLASSES]) |
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interpreter.allocate_tensors() |
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interpreter.set_tensor(input_details[0]["index"], attention_mask) |
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interpreter.set_tensor(input_details[1]["index"], input_ids) |
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interpreter.invoke() |
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tflite_pred = interpreter.get_tensor(output_details["index"]) |
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tflite_pred = parse_prediction(tflite_pred) |
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return tflite_pred |
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def cols_check(new_cols, old_cols): |
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return all([new_col==old_col for new_col, old_col in zip(new_cols, old_cols)]) |
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def predict_news_category(old_news: pd.DataFrame, new_news: pd.DataFrame, interpreter, label_encoder, tokenizer): |
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old_news = old_news.copy() |
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new_news = new_news.copy() |
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old_news.drop(columns='_id', inplace=True) |
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if 'category' not in [*old_news.columns]: |
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print('no prior predictions found') |
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if not cols_check([*new_news.columns], [*old_news.columns]): |
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raise Exeption("New and old cols don't match") |
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final_df = pd.concat([old_news, new_news], axis=0, ignore_index=True) |
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final_df.drop_duplicates(subset='url', keep='first', inplace=True) |
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headlines = [*final_df['title']].copy() |
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label, prob = inference(headlines, interpreter, label_encoder, tokenizer) |
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final_df['category'] = label |
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final_df['pred_proba'] = prob |
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else: |
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print('prior predictions found') |
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if not cols_check([*new_news.columns], [*old_news.columns][:-2]): |
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raise Exeption("New and old cols don't match") |
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old_urls = [*old_news['url']] |
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new_news = new_news.loc[new_news['url'].isin(old_urls) == False, :] |
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headlines = [*new_news['title']].copy() |
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label, prob = inference(headlines, interpreter, label_encoder, tokenizer) |
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new_news['category'] = label |
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new_news['pred_proba'] = prob |
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final_df = pd.concat([old_news, new_news], axis=0, ignore_index=True) |
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final_df.drop_duplicates(subset='url', keep='first', inplace=True) |
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final_df.reset_index(drop=True, inplace=True) |
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final_df.loc[final_df['pred_proba']<0.65, 'category'] = 'OTHERS' |
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return final_df |
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