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import streamlit as st | |
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.naive_bayes import GaussianNB | |
from sklearn.svm import SVC | |
from xgboost import XGBClassifier | |
def train_selected_model(X_train, Y_train, model_type, model_params=None): | |
""" | |
Trains a specific classification model based on the provided model type and parameters. | |
Parameters: | |
- X_train (array-like): The training input samples. | |
- Y_train (array-like): The target labels for classification. | |
- model_type (int): Specifies the type of classification model to be trained. | |
1 for Logistic Regression, 2 for Support Vector Machine (SVM), 3 for Naive Bayes, | |
4 for Random Forest, 5 for AdaBoost, 6 for XGBoost, and 7 for Gradient Boosting. | |
- model_params (dict, optional): A dictionary of parameters for the model. Defaults to None. | |
Returns: | |
- model: The trained model object based on the specified type. | |
""" | |
if model_type == 1: | |
return LogisticRegression_train(X_train, Y_train, model_params) | |
elif model_type == 2: | |
return SVM_train(X_train, Y_train, model_params) | |
elif model_type == 3: | |
return NaiveBayes_train(X_train, Y_train, model_params) | |
elif model_type == 4: | |
return RandomForest_train(X_train, Y_train, model_params=model_params) | |
elif model_type == 5: | |
return AdaBoost_train(X_train, Y_train, model_params) | |
elif model_type == 6: | |
return XGBoost_train(X_train, Y_train, model_params) | |
elif model_type == 7: | |
return GradientBoosting_train(X_train, Y_train, model_params) | |
def LogisticRegression_train(X_train, Y_train, model_params=None): | |
if model_params is None: model_params = {} | |
logreg = LogisticRegression(**model_params) | |
logreg.fit(X_train, Y_train) | |
return logreg | |
def SVM_train(X_train, Y_train, model_params=None): | |
if model_params is None: model_params = {} | |
svm = SVC(**model_params) | |
svm.fit(X_train, Y_train) | |
return svm | |
def NaiveBayes_train(X_train, Y_train, model_params=None): | |
if model_params is None: model_params = {} | |
nb = GaussianNB(**model_params) | |
nb.fit(X_train, Y_train) | |
return nb | |
def RandomForest_train(X_train, Y_train, n_estimators=100, random_state=None, model_params=None): | |
if model_params is None: model_params = {} | |
rf_params = {'n_estimators': n_estimators, 'random_state': random_state} | |
rf_params.update(model_params) | |
rf = RandomForestClassifier(**rf_params) | |
rf.fit(X_train, Y_train) | |
return rf | |
def AdaBoost_train(X_train, Y_train, model_params=None): | |
if model_params is None: model_params = {} | |
ab = AdaBoostClassifier(**model_params) | |
ab.fit(X_train, Y_train) | |
return ab | |
def XGBoost_train(X_train, Y_train, model_params=None): | |
if model_params is None: model_params = {} | |
xgb = XGBClassifier(**model_params) | |
xgb.fit(X_train, Y_train) | |
return xgb | |
def GradientBoosting_train(X_train, Y_train, model_params=None): | |
if model_params is None: model_params = {} | |
gb = GradientBoostingClassifier(**model_params) | |
gb.fit(X_train, Y_train) | |
return gb |