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| import re | |
| from nltk.stem import PorterStemmer, WordNetLemmatizer | |
| # Keyword Extraction | |
| def expand_list_of_lists(list_of_lists): | |
| """ | |
| Expands a list of lists of strings to a list of strings. | |
| Args: | |
| list_of_lists: A list of lists of strings. | |
| Returns: | |
| A list of strings. | |
| """ | |
| expanded_list = [] | |
| for inner_list in list_of_lists: | |
| for string in inner_list: | |
| expanded_list.append(string) | |
| return expanded_list | |
| def keywords_no_companies(texts): | |
| # Company list (to remove companies from extracted entities) | |
| company_list = [ | |
| "apple", | |
| "amd", | |
| "amazon", | |
| "cisco", | |
| "google", | |
| "microsoft", | |
| "nvidia", | |
| "asml", | |
| "intel", | |
| "micron", | |
| "aapl", | |
| "csco", | |
| "msft", | |
| "asml", | |
| "nvda", | |
| "googl", | |
| "mu", | |
| "intc", | |
| "amzn", | |
| "amd", | |
| ] | |
| texts = [text.split(" ") for text in texts] | |
| texts = expand_list_of_lists(texts) | |
| # Convert all strings to lowercase. | |
| lower_texts = [text.lower() for text in texts] | |
| keywords = [text for text in lower_texts if text not in company_list] | |
| return keywords | |
| def all_keywords_combs(texts): | |
| texts = [text.split(" ") for text in texts] | |
| texts = expand_list_of_lists(texts) | |
| # Convert all strings to lowercase. | |
| lower_texts = [text.lower() for text in texts] | |
| # Stem the words in each string. | |
| stemmer = PorterStemmer() | |
| stem_texts = [stemmer.stem(text) for text in texts] | |
| # Lemmatize the words in each string. | |
| lemmatizer = WordNetLemmatizer() | |
| lemm_texts = [lemmatizer.lemmatize(text) for text in texts] | |
| texts.extend(lower_texts) | |
| texts.extend(stem_texts) | |
| texts.extend(lemm_texts) | |
| return texts | |
| def extract_keywords(query_text, model): | |
| prompt = "###Instruction: Identify the key entities that accurately describe the context.\n\nInput:{query_text}\n\n###Response:" | |
| #prompt = f"###Instruction:Extract the important keywords which describe the context accurately.\n\nInput:{query_text}\n\n###Response:" | |
| response = model.predict(prompt) | |
| keywords = response.split(", ") | |
| keywords = keywords_no_companies(keywords) | |
| return keywords | |
| # Entity Extraction | |
| def generate_alpaca_ner_prompt(query): | |
| prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Use the following guidelines to extract the entities representing the Company, Quarter, and Year in the sentence. | |
| ### Instruction: | |
| - The output should be in the form "Company - Value, Quarter - Value, Year - Value". | |
| - The output should be in the form "Company - None, Quarter - None, Year - None", if no entities are found. | |
| - Only use entities that exist in the final sentence. | |
| - If Company cannot be found in the sentence, return "none" for that entity. | |
| - If Quarter cannot be found in the sentence, return "none" for that entity. | |
| - If Year cannot be found in the sentence, return "none" for that entity. | |
| - If there is ambiguity finding the entity, return "none" for that entity. | |
| ### Input: | |
| What was discussed regarding Services revenue performance in Apple's Q3 2020 earnings call? | |
| Company - Apple, Quarter - Q3, Year - 2020 | |
| How has the growth in Q1 been for the consumer market as seen by AMD? | |
| Company - AMD, Quarter - Q1, Year - none | |
| What was the long term view on GOOGL's cloud business growth as discussed in their earnings call? | |
| Company - Google, Quarter - none, Year - none | |
| What is Nvidia's outlook in the data center business in Q3 2020? | |
| Company - Nvidia, Quarter - Q3, Year - 2020 | |
| What are the expansion plans of Amazon in the Asia Pacific region as discussed in their earnings call? | |
| Company - Amazon, Quarter - none, Year - none | |
| What did the Analysts ask about CSCO's cybersecurity business in the earnings call in 2016? | |
| Company - Cisco, Quarter - none, Year - 2016 | |
| {query} | |
| ### Response:""" | |
| return prompt | |
| def format_entities_flan_alpaca(values): | |
| """ | |
| Extracts the text for each entity from the output generated by the | |
| Flan-Alpaca model. | |
| """ | |
| try: | |
| company_string, quarter_string, year_string = values.split(", ") | |
| except: | |
| company = None | |
| quarter = None | |
| year = None | |
| try: | |
| company = company_string.split(" - ")[1].lower() | |
| company = None if company.lower() == "none" else company | |
| except: | |
| company = None | |
| try: | |
| quarter = quarter_string.split(" - ")[1] | |
| quarter = None if quarter.lower() == "none" else quarter | |
| except: | |
| quarter = None | |
| try: | |
| year = year_string.split(" - ")[1] | |
| year = None if year.lower() == "none" else year | |
| except: | |
| year = None | |
| print((company, quarter, year)) | |
| return company, quarter, year | |
| def extract_quarter_year(string): | |
| # Extract year from string | |
| year_match = re.search(r"\d{4}", string) | |
| if year_match: | |
| year = year_match.group() | |
| else: | |
| year = None | |
| # Extract quarter from string | |
| quarter_match = re.search(r"Q\d", string) | |
| if quarter_match: | |
| quarter = "Q" + quarter_match.group()[1] | |
| else: | |
| quarter = None | |
| return quarter, year | |
| def extract_ticker_spacy(query, model): | |
| doc = model(query) | |
| entities = {ent.label_: ent.text for ent in doc.ents} | |
| print(entities.keys()) | |
| if "ORG" in entities.keys(): | |
| company = entities["ORG"].lower() | |
| else: | |
| company = None | |
| return company | |
| def clean_entities(company, quarter, year): | |
| company_ticker_map = { | |
| "apple": "AAPL", | |
| "amd": "AMD", | |
| "amazon": "AMZN", | |
| "cisco": "CSCO", | |
| "google": "GOOGL", | |
| "microsoft": "MSFT", | |
| "nvidia": "NVDA", | |
| "asml": "ASML", | |
| "intel": "INTC", | |
| "micron": "MU", | |
| } | |
| ticker_choice = [ | |
| "AAPL", | |
| "CSCO", | |
| "MSFT", | |
| "ASML", | |
| "NVDA", | |
| "GOOGL", | |
| "MU", | |
| "INTC", | |
| "AMZN", | |
| "AMD", | |
| ] | |
| year_choice = ["2020", "2019", "2018", "2017", "2016", "All"] | |
| quarter_choice = ["Q1", "Q2", "Q3", "Q4", "All"] | |
| if company is not None: | |
| if company in company_ticker_map.keys(): | |
| ticker = company_ticker_map[company] | |
| ticker_index = ticker_choice.index(ticker) | |
| else: | |
| ticker_index = 0 | |
| else: | |
| ticker_index = 0 | |
| if quarter is not None: | |
| if quarter in quarter_choice: | |
| quarter_index = quarter_choice.index(quarter) | |
| else: | |
| quarter_index = len(quarter_choice) - 1 | |
| else: | |
| quarter_index = len(quarter_choice) - 1 | |
| if year is not None: | |
| if year in year_choice: | |
| year_index = year_choice.index(year) | |
| else: | |
| year_index = len(year_choice) - 1 | |
| else: | |
| year_index = len(year_choice) - 1 | |
| return ticker_index, quarter_index, year_index | |