128 lines
4.9 KiB
Python
128 lines
4.9 KiB
Python
from datetime import datetime
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from collections import Counter
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import numpy as np
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import pandas as pd
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from sklearn.ensemble import RandomForestClassifier
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from sqlalchemy import select
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from data.database import Base, dal, project_variables
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import util.drawing as drawutil
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RANDOM_SEED = 42
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def load_dataframe(
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table_name: str, start_date: datetime | None = None, end_date: datetime | None = None
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) -> pd.DataFrame:
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"""
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:param table_name: the name of the table to load data from
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:param start_date: the start date for the data
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:param end_date: the end date for the data
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:returns: a pandas DataFrame containing the data
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"""
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dal.connect()
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session = dal.Session()
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game_dict = drawutil.check_table_name(table_name=table_name)
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if game_dict:
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target_table = Base.metadata.tables.get(game_dict['db_table_name'])
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if start_date is not None and end_date is not None:
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sql_statement = (select(target_table)
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.where(target_table.columns.draw_date >= start_date)
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.where(target_table.columns.draw_date <= end_date))
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elif start_date is not None and end_date is None:
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sql_statement = (select(target_table).where(target_table.columns.draw_date >= start_date))
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elif start_date is None and end_date is not None:
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sql_statement = (select(target_table).where(target_table.columns.draw_date <= end_date))
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else:
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sql_statement = select(target_table)
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return pd.read_sql(sql_statement, session.bind)
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else:
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raise ValueError('An invalid table name was provided')
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def get_most_common_number(
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data_frame: pd.DataFrame, columns: list | None = None, top: int = 1
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) -> list[int]:
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"""
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:param data_frame: a pandas DataFrame containing the data
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:param columns: a list of column names to use
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:param top: the number of top (most seen) numbers to return
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:returns: a list of the most common numbers
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"""
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if columns is None:
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columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
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flat_numbers = data_frame[columns].values.flatten()
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counts = Counter(flat_numbers)
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return [int(num) for num, _ in counts.most_common(top)]
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def get_least_common_number(
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data_frame: pd.DataFrame, columns: list | None = None, bottom: int = 1
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) -> list[int]:
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"""
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:param data_frame: a pandas DataFrame containing the data
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:param columns: a list of column names to use
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:param bottom: the number of bottom (least seen) numbers to return
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:returns: a list of the least common numbers
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"""
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if columns is None:
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columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
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flat_numbers = data_frame[columns].values.flatten()
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counts = Counter(flat_numbers)
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return list(reversed([int(num) for num, _ in counts.most_common()[-bottom:]]))
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def calculate_probabilities(
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data_frame: pd.DataFrame, max_number: int, columns: list | None = None
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) -> dict[int, float]:
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"""
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:param data_frame: A pandas DataFrame containing the data to calculate probabilities for
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:param max_number: The maximum number possible in the data_frame
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:param columns: The list of column names to use from the data_frame
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:returns dict: A dictionary containing the probabilities
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"""
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if columns is None:
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columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
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if data_frame.empty:
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return dict()
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# get all the numbers in the groups
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all_numbers = [num for group in data_frame[columns].values for num in group]
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# count all the occurrences of each number
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counts = Counter(all_numbers)
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# calculate the basic probability of each number occurring again
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probabilities = {num: counts.get(num, 0) / (max_number + 1) for num in range(1, max_number + 1)}
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# if any calculation is greater than one, use what is to the right of the decimal point as the value
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for key, value in probabilities.items():
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if value > 1:
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probabilities[key] = value - int(str(value).split('.')[0])
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# return the probability dict
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return probabilities
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def get_hot_numbers(probabilities: dict[int, float], top: int = 5) -> list[tuple[int, float]]:
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"""
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:param probabilities: A dictionary containing the probabilities
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:param top: The count of hottest items to return, defaults to 5
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:returns list of tuples: A list of the hot numbers and their raw score
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"""
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return sorted(probabilities.items(), key=lambda item: item[1], reverse=True)[:top]
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def get_cold_numbers(probabilities: dict[int, float], bottom: int = 5) -> list[tuple[int, float]]:
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"""
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:param probabilities: A dictionary containing the probabilities
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:param bottom: The count of coldest items to return, defaults to 5
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:returns list of tuples: A list of the hot numbers and their raw score
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"""
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return sorted(probabilities.items(), key=lambda item: item[1])[:bottom]
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