game.py

# """
# Semantrix Game Module

# This module defines the Semantrix class, which implements a word guessing game using word embeddings. The game can be configured to use either a Word2Vec model or a SentenceTransformer model for word embeddings. The game supports multiple languages and difficulty levels.

# Classes:
#     Semantrix: A class that implements the Semantrix word guessing game.
#     Semantrix.DictWrapper: A helper class to wrap configuration dictionaries.

# Functions:
#     __init__(self, lang=0, model_type="SentenceTransformer"): Initializes the Semantrix game with the specified language and model type.
#     prepare_game(self, difficulty): Prepares the game with the selected difficulty level.
#     gen_rank(self, repeated): Generates the ranking file based on the scores.
#     play_game(self, word): Plays the game with the selected word and returns feedback.
#     curiosity(self): Generates a curiosity hint about the secret word once the game is over.

# Attributes:
#     model (KeyedVectors): The word embeddings model.
#     config_file_path (str): Path to the configuration file.
#     secret_file_path (str): Path to the secret words file.
#     data_path (str): Path to the data directory.
#     Config_full (dict): Full configuration data.
#     secret (dict): Secret words data.
#     lang (int): Language of the game (0 for Spanish, 1 for English).
#     model_type (str): Type of the model ("word2vec" or "SentenceTransformer").
#     Config (DictWrapper): Configuration data for the selected language.
#     secret_dict (dict): Secret words for the selected language.
#     secret_list (list): List of secret words for the selected difficulty.
#     words (list): List of words guessed by the player.
#     scores (list): List of scores for the guessed words.
#     win (bool): Indicates if the player has won the game.
#     n (int): Number of hints given.
#     recent_hint (int): Counter for recent hints.
#     f_dev_avg (float): Moving average of the tendency slope.
#     last_hint (int): Index of the last hint given.
#     difficulty (int): Difficulty level of the game.
# """

import json
import random
from datetime import datetime
import numpy as np
from gensim.models import KeyedVectors
from hints import curiosity, hint
from tracking import (
    calculate_moving_average,
    calculate_tendency_slope,
)
from sentence_transformers import SentenceTransformer
import warnings

warnings.filterwarnings(action="ignore", category=UserWarning, module="gensim")


# Define the class Semantrix
class Semantrix:

    # Define the paths for the configuration files and the data
    config_file_path = "config/lang.json"
    secret_file_path = "config/secret.json"
    data_path = "data/"

    # Define the class DictWrapper to store the configuration data
    class DictWrapper:
        def __init__(self, data_dict):
            self.__dict__.update(data_dict)

    # Define the constructor of the class which loads the configuration files and initializes the class variables depending on the language parameter and the model type
    def __init__(self, lang=0, model_type="SentenceTransformer"):

        # Load the configuration files
        with open(self.config_file_path, "r") as file:
            self.Config_full = json.load(file)

        # Load the secret file where the secret words are stored
        with open(self.secret_file_path, "r") as file:
            self.secret = json.load(file)

        # Set the language of the game
        self.lang = lang

        # Set the model type
        self.model_type = model_type

        if self.lang == 1:
            self.model = KeyedVectors.load("config/w2v_models/eng_w2v_model", mmap="r")
            self.Config = self.DictWrapper(self.Config_full["ENG"]["Game"])
            self.secret_dict = self.secret["ENG"]
        else:
            self.model = KeyedVectors.load("config/w2v_models/esp_w2v_model", mmap="r")
            self.Config = self.DictWrapper(self.Config_full["SPA"]["Game"])
            self.secret_dict = self.secret["SPA"]

        # Create empty KeyedVectors model with predefined size where the embeddings will be stored if Sentence Transformer used
        if self.model_type == "SentenceTransformer":
            self.model_trans = KeyedVectors(768)

        self.model_st = SentenceTransformer(
            "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
        )

        # Create the ranking file
        with open(self.data_path + "ranking.txt", "w+") as file:
            file.write("---------------------------")

    # Define the function to prepare the game with the selected difficulty
    def prepare_game(self, difficulty):

        # Set the secret list depending on the difficulty
        self.secret_list = (
            self.secret_dict["basic"]
            if difficulty <= 2
            else self.secret_dict["advanced"]
        )

        # Select a random secret word from the secret list
        self.secret = self.secret_list.pop(random.randint(0, len(self.secret_list) - 1))
        self.secret = self.secret.lower()

        # Store the secret word in the words list
        self.words = [self.Config.secret_word]

        # Store the score in the scores list
        self.scores = [10]

        # Store the embedding of the secret word in the embeddings dictionary
        if self.model_type == "SentenceTransformer":
            # Add the secret word to the KeyedVectors model if the model type is SentenceTransformer
            # If the model type is word2vec, the secret word is already in the model
            if self.secret not in self.model_trans.key_to_index.keys():
                self.model_trans.add_vector(
                    self.secret,
                    self.model_st.encode(self.secret, convert_to_tensor=True).tolist(),
                )

        # Initialize the game variables
        self.win = False
        self.n = 0
        self.recent_hint = 0
        self.f_dev_avg = 0
        self.last_hint = -1
        self.difficulty = difficulty

        # Set the number of hints depending on the difficulty
        if self.difficulty == 1:
            self.n = 3

    # Define the function to generate the ranking file
    def gen_rank(self, repeated):
        ascending_indices = np.argsort(self.scores)
        descending_indices = list(ascending_indices[::-1])
        ranking_data = []
        k = len(self.words) - 1
        if repeated != -1:
            k = repeated

        ranking_data.append(["#" + str(k), self.words[k], self.scores[k]])

        ranking_data.append("---------------------------")
        for i in descending_indices:
            if i == 0:
                continue
            ranking_data.append(["#" + str(i), self.words[i], self.scores[i]])

        with open(self.data_path + "ranking.txt", "w+") as file:
            for item in ranking_data:
                file.write("%s\n" % item)

    # Define the function to play the game with the selected word
    def play_game(self, word):

        # Convert the word to lowercase
        word = word.lower()

        # Check if the user wants to give up
        if word == "give_up":
            text = (
                "[lose]"
                + self.Config.Feedback_9
                + self.secret
                + "\n\n"
                + self.Config.Feedback_10
            )
            return text

        # Check if the word is repeated
        if word in self.words:
            repeated = self.words.index(word)
        else:
            repeated = -1
            self.words.append(word)

        # Check if the word is in the model already
        if word not in self.model.key_to_index.keys():
            # If the word is not in the model, remove it from the words list and provide feedback
            self.words.pop(len(self.words) - 1)
            feedback = (
                "I don't know that word. Try again."
                if self.lang == 1
                else "No conozco esa palabra. Inténtalo de nuevo."
            )
            feedback += (
                "[rank]" + open(self.data_path + "ranking.txt", "r").read()
                if len(self.words) > 1
                else "\n\n"
            )
            return feedback

        # Add the word to the KeyedVectors model if the model type is SentenceTransformer and the word is not already in the model
        if (
            self.model_type == "SentenceTransformer"
            and word not in self.model_trans.key_to_index.keys()
        ):
            self.model_trans.add_vector(
                word, self.model_st.encode(word, convert_to_tensor=True).tolist()
            )

        # Calculate the score of the word, apply logarithmic scaling, interpolate the score to a range from 0 to 10, and round it to two decimal places
        if self.model_type == "word2vec":
            similarity = self.model.similarity(self.secret, word)
        else:
            similarity = self.model_trans.similarity(self.secret, word)

        log_similarity = np.log10((similarity if similarity > 0 else 0) * 10)
        score = round(
            np.interp(
                log_similarity,
                [0, np.log10(10)],
                [0, 10],
            ),
            2,
        )

        # Remove the word from the score list if it is repeated
        if repeated == -1:
            self.scores.append(score)

        # Generate the feedback message depending on the score
        if score <= 2.5:
            feedback = self.Config.Feedback_0 + str(score)
        elif score > 2.5 and score <= 4.0:
            feedback = self.Config.Feedback_1 + str(score)
        elif score > 4.0 and score <= 6.0:
            feedback = self.Config.Feedback_2 + str(score)
        elif score > 6.0 and score <= 7.5:
            feedback = self.Config.Feedback_3 + str(score)
        elif score > 7.5 and score <= 8.0:
            feedback = self.Config.Feedback_4 + str(score)
        elif score > 8.0 and score < 10.0:
            feedback = self.Config.Feedback_5 + str(score)
        # If the score is 10, the user wins the game
        else:
            self.win = True
            feedback = "[win]" + self.Config.Feedback_8
            self.words[0] = self.secret
            self.words.pop(len(self.words) - 1)
            self.scores.pop(len(self.scores) - 1)

        # Generate the feedback message depending on the score and the previous score
        if score > self.scores[len(self.scores) - 2] and self.win == False:
            feedback += "\n" + self.Config.Feedback_6
        elif score < self.scores[len(self.scores) - 2] and self.win == False:
            feedback += "\n" + self.Config.Feedback_7

        ## Hint generation
        # If the difficulty is not 4, calculate the moving average of the scores and the tendency slope
        if self.difficulty != 4:
            mov_avg = calculate_moving_average(self.scores[1:], 5)

            # If the moving average has more than one element and the user has not won yet, calculate the tendency slope and the moving average of the tendency slope
            if len(mov_avg) > 1 and self.win == False:
                f_dev = calculate_tendency_slope(mov_avg)
                f_dev_avg = calculate_moving_average(f_dev, 3)

                # If the tendency slope is negative and the hint has not been given recently (at least three rounds earlier), generate a hint
                if f_dev_avg[len(f_dev_avg) - 1] < 0 and self.recent_hint == 0:

                    # Generate a random hint intro from the hint list
                    i = random.randint(0, len(self.Config.hint_intro) - 1)
                    feedback += "\n\n[hint]" + self.Config.hint_intro[i]

                    # Generate a dynamic hint
                    hint_text, self.n, self.last_hint = hint(
                        self.secret,
                        self.n,
                        self.model_st,
                        self.last_hint,
                        self.lang,
                        (
                            self.DictWrapper(self.Config_full["ENG"]["Hint"])
                            if self.lang == 1
                            else self.DictWrapper(self.Config_full["SPA"]["Hint"])
                        ),
                    )
                    feedback += "\n" + hint_text
                    self.recent_hint = 3

            if self.recent_hint != 0:
                self.recent_hint -= 1

        # Generate the ranking file
        self.gen_rank(repeated)

        # Add the ranking file to the feedback message
        feedback += "[rank]" + open(self.data_path + "ranking.txt", "r").read()

        # Save the ranking file with the plays of the user if the user wins
        if self.win:

            with open(self.data_path + "ranking.txt", "r") as original_file:
                file_content = original_file.readlines()

            new_file_name = self.secret + "_" + str(datetime.now()) + ".txt"

            with open(self.data_path + "plays/" + new_file_name, "w+") as new_file:
                new_file.writelines(file_content[2:])

        # Return the feedback message
        return feedback

    # Define the function to generate a curiosity hint once the game is over
    def curiosity(self):

        # Generate a curiosity aboyt the secret word
        feedback = curiosity(
            self.secret,
            (
                self.DictWrapper(self.Config_full["ENG"]["Hint"])
                if self.lang == 1
                else self.DictWrapper(self.Config_full["SPA"]["Hint"])
            ),
        )

        # Return the feedback message
        return feedback