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| from sklearn.pipeline import Pipeline | |
| from sklearn.compose import ColumnTransformer | |
| from sklearn.preprocessing import OneHotEncoder, StandardScaler | |
| from sklearn.impute import SimpleImputer | |
| from sklearn.model_selection import train_test_split, GridSearchCV | |
| from sklearn.naive_bayes import GaussianNB | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.svm import SVC | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.cluster import KMeans | |
| from sklearn.metrics import accuracy_score, classification_report | |
| class ClassificationModels: | |
| def __init__(self, X, y=None, hyperparameters=None): | |
| self.X = X | |
| self.y = y | |
| self.hyperparameters = hyperparameters | |
| def split_data(self, test_size=0.2, random_state=42): | |
| self.X_train, self.X_test, self.y_train, self.y_test = train_test_split( | |
| self.X, self.y, test_size=test_size, random_state=random_state | |
| ) | |
| def build_preprocessor(self): | |
| # Separate numerical and categorical columns | |
| numeric_features = self.X.select_dtypes(include=['int64', 'float64']).columns | |
| categorical_features = self.X.select_dtypes(include=['object']).columns | |
| # Define transformers for numerical and categorical data | |
| numeric_transformer = Pipeline(steps=[ | |
| ('imputer', SimpleImputer(strategy='mean')), | |
| ('scaler', StandardScaler()) | |
| ]) | |
| categorical_transformer = Pipeline(steps=[ | |
| ('imputer', SimpleImputer(strategy='most_frequent')), | |
| ('onehot', OneHotEncoder(handle_unknown='ignore')) | |
| ]) | |
| # Combine transformers using ColumnTransformer | |
| preprocessor = ColumnTransformer( | |
| transformers=[ | |
| ('num', numeric_transformer, numeric_features), | |
| ('cat', categorical_transformer, categorical_features) | |
| ]) | |
| return preprocessor | |
| def build_model_pipeline(self, classifier): | |
| # Build preprocessor | |
| preprocessor = self.build_preprocessor() | |
| # Combine preprocessor with classifier in a pipeline | |
| model_pipeline = Pipeline(steps=[ | |
| ('preprocessor', preprocessor), | |
| ('classifier', classifier) | |
| ]) | |
| return model_pipeline | |
| def evaluate_model(self, model): | |
| model.fit(self.X_train, self.y_train) | |
| accuracy = model.score(self.X_test, self.y_test) | |
| return accuracy | |
| def evaluate_classification_report(self, model): | |
| y_pred = model.predict(self.X_test) | |
| return classification_report(self.y_test, y_pred, output_dict=True) | |
| def naive_bayes_classifier(self,params = None): | |
| model = GaussianNB() | |
| return self.build_model_pipeline(model) | |
| def logistic_regression(self, params=None): | |
| model = LogisticRegression() | |
| if self.hyperparameters and 'logistic_regression' in self.hyperparameters: | |
| model = GridSearchCV(model, params, cv=5) | |
| return self.build_model_pipeline(model) | |
| def decision_tree(self, params=None): | |
| model = DecisionTreeClassifier() | |
| if self.hyperparameters and 'decision_tree' in self.hyperparameters: | |
| model = GridSearchCV(model, params=self.hyperparameters['decision_tree'], cv=5) | |
| return self.build_model_pipeline(model) | |
| def random_forests(self, params=None): | |
| model = RandomForestClassifier() | |
| if self.hyperparameters and 'random_forests' in self.hyperparameters: | |
| model = GridSearchCV(model, params=self.hyperparameters['random_forests'], cv=5) | |
| return self.build_model_pipeline(model) | |
| def support_vector_machines(self, params=None): | |
| model = SVC() | |
| if self.hyperparameters and 'support_vector_machines' in self.hyperparameters: | |
| model = GridSearchCV(model, params=self.hyperparameters['support_vector_machines'], cv=5) | |
| return self.build_model_pipeline(model) | |
| def k_nearest_neighbour(self, params=None): | |
| model = KNeighborsClassifier() | |
| if self.hyperparameters and 'k_nearest_neighbour' in self.hyperparameters: | |
| model = GridSearchCV(model, params=self.hyperparameters['k_nearest_neighbour'], cv=5) | |
| return self.build_model_pipeline(model) | |
| def k_means_clustering(self, n_clusters): | |
| model = KMeans(n_clusters=n_clusters) | |
| return model | |
| def evaluate_model(self, model): | |
| model.fit(self.X_train, self.y_train) | |
| accuracy = model.score(self.X_test, self.y_test) | |
| return accuracy | |
| def evaluate_classification_report(self, model): | |
| y_pred = model.predict(self.X_test) | |
| return classification_report(self.y_test, y_pred, output_dict=True) | |
| def predict_output(self, model): | |
| return model.predict(self.X_test) | |
| """ | |
| from sklearn.model_selection import train_test_split, GridSearchCV | |
| from sklearn.naive_bayes import GaussianNB | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.svm import SVC | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.cluster import KMeans | |
| from sklearn.metrics import accuracy_score | |
| from sklearn.metrics import classification_report | |
| class ClassificationModels: | |
| def __init__(self, X, y= None,hyperparameters=None): | |
| self.X = X | |
| self.y = y | |
| self.hyperparameters = hyperparameters | |
| def split_data(self, test_size=0.2, random_state=42): | |
| self.X_train, self.X_test, self.y_train, self.y_test = train_test_split( | |
| self.X, self.y, test_size=test_size, random_state=random_state | |
| ) | |
| def naive_bayes_classifier(self, param = None): | |
| model = GaussianNB() | |
| model.fit(self.X_train, self.y_train) | |
| return model | |
| def logistic_regression(self, params=None): | |
| model = LogisticRegression() | |
| if self.hyperparameters and 'logistic_regression' in self.hyperparameters: | |
| model = GridSearchCV(model, params, cv=5) | |
| model.fit(self.X_train, self.y_train) | |
| return model | |
| def decision_tree(self, params=None): | |
| model = DecisionTreeClassifier() | |
| if self.hyperparameters and 'decision_tree' in self.hyperparameters: | |
| model = GridSearchCV(model, params =self.hyperparameters['decision_tree'], cv=5) | |
| model.fit(self.X_train, self.y_train) | |
| return model | |
| def random_forests(self, params=None): | |
| model = RandomForestClassifier() | |
| if self.hyperparameters and 'random_forests' in self.hyperparameters: | |
| model = GridSearchCV(model, params= self.hyperparameters['random_forests'], cv=5) | |
| model.fit(self.X_train, self.y_train) | |
| return model | |
| def support_vector_machines(self, params=None): | |
| model = SVC() | |
| if self.hyperparameters and 'support_vector_machines' in self.hyperparameters: | |
| model = GridSearchCV(model, params= self.hyperparameters['support_vector_machines'], cv=5) | |
| model.fit(self.X_train, self.y_train) | |
| return model | |
| def k_nearest_neighbour(self, params=None): | |
| model = KNeighborsClassifier() | |
| if self.hyperparameters and 'k_nearest_neighbour' in self.hyperparameters: | |
| st.write(self.hyperparameters['k_nearest_neighbour']) | |
| model = GridSearchCV(model, params = self.hyperparameters['k_nearest_neighbour'], cv=5) | |
| model.fit(self.X_train, self.y_train) | |
| return model | |
| def k_means_clustering(self, n_clusters): | |
| model = KMeans(n_clusters=n_clusters) | |
| model.fit(self.X_train) | |
| return model | |
| def evaluate_model(self, model): | |
| y_pred = model.predict(self.X_test) | |
| accuracy = accuracy_score(self.y_test, y_pred) | |
| return accuracy | |
| def evaluate_classification_report(self,model): | |
| y_pred = model.predict(self.X_test) | |
| return classification_report(self.y_test, y_pred, output_dict=True) | |
| def predict_output(self, model): | |
| y_pred = model.predict(self.X_test) | |
| return y_pred | |
| """ |