Spaces:
Runtime error
Runtime error
File size: 2,930 Bytes
92b63f0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
import logging
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from typing import Any
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class ModelBuilding:
def logistic_regression(self, X_train, y_train) -> Any:
"""Initialize, fit, and return a Logistic Regression model."""
logger.info("Initializing Logistic Regression model...")
model = LogisticRegression()
model.fit(X_train, y_train)
logger.info("Logistic Regression model trained successfully.")
return model
def xgboost(self, X_train, y_train) -> Any:
"""Initialize, fit, and return a Naive Bayes classifier model."""
logger.info("Initializing xgboost model...")
model = XGBClassifier()
model.fit(X_train, y_train)
logger.info("xgboost model trained successfully.")
return model
def random_forest(self, X_train, y_train) -> Any:
"""Initialize, fit, and return a Random Forest classifier model."""
logger.info("Initializing Random Forest model...")
model = RandomForestClassifier()
model.fit(X_train, y_train)
logger.info("Random Forest model trained successfully.")
return model
def decision_tree(self, X_train, y_train) -> Any:
"""Initialize, fit, and return a Decision Tree classifier model."""
logger.info("Initializing Decision Tree model...")
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
logger.info("Decision Tree model trained successfully.")
return model
def get_model(self, model_name: str, X_train, y_train) -> Any:
"""
Initialize, fit, and return a machine learning model by name.
Parameters:
model_name : str
The name of the model to create.
X_train : pd.DataFrame
The feature data to train the model on.
y_train : pd.Series
The target data to train the model on.
Returns :
model : Any
The trained model instance.
Raises:
ValueError
If the model name is not recognized.
"""
if model_name == "logistic_regression":
return self.logistic_regression(X_train, y_train)
elif model_name == "xgboost":
return self.xgboost(X_train, y_train)
elif model_name == "random_forest":
return self.random_forest(X_train, y_train)
elif model_name == "decision_tree":
return self.decision_tree(X_train, y_train)
else:
logger.error(f"Model '{model_name}' not recognized.")
raise ValueError(f"Model '{model_name}' not recognized.")
|