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: