import datetime as dt from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler from football_trading.src.models.templates.sklearn_model import SKLearnModel from football_trading.src.utils.logging import get_logger logger = get_logger() class LogisticRegressionModel(SKLearnModel): """Everything specific to LogisticRegression models goes in this class """ def __init__( self, test_mode=False, load_model=False, load_model_date=None, save_trained_model=True, upload_historic_predictions=None, ): # Call the __init__ method of the parent class super().__init__( test_mode=test_mode, upload_historic_predictions=upload_historic_predictions, ) # Define the model object self.model_object = LogisticRegression # The name of the model you want ot use self.model_type = self.model_object.__name__
from dash.dependencies import Input, Output from football_trading.settings import model_dir, tmp_dir, LOCAL, DB_DIR, S3_BUCKET_NAME, \ AWS_SECRET_ACCESS_KEY, AWS_ACCESS_KEY_ID, training_data_dir from football_trading.src.utils.logging import get_logger from football_trading.src.utils.dashboard import ( get_form_dif_view, get_team_home_away_performance, get_performance_by_season, get_cumulative_profit_view, get_cumulative_profit_from_bof, get_historic_features, load_production_model) from football_trading.src.utils.db import run_query from football_trading.src.utils.s3_tools import download_from_s3 active_graphs = ['profit-by-date', 'accuracy_home_away', 'accuracy_over_time', 'form_diff_acc'] logger = get_logger(logger_name='dashboard') def get_dashboard_app(server=None): session = boto3.session.Session( aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY ) if not LOCAL: logger.info('Attempting to download DB from S3..') try: if not os.path.exists(f"{DB_DIR}"): download_from_s3(local_path=f"{DB_DIR}", s3_path='db.sqlite', bucket=S3_BUCKET_NAME) except Exception as e: