feat(Models): added lightGBM, moved other models to separate files (#128)

* feat(Models): added lightGBM, moved other models to separate files

* feat(Models): added non-working statsmodel wrapper

* fix(Models): added work-in-progress comment to StatsModels

models/base.py

@@ -1,14 +1,9 @@
 from __future__ import annotations
 from typing import Literal, Optional, Union
-from sklearn.base import clone
 from abc import ABC, abstractmethod
 import numpy as np
 
-import copy
-import pytorch_lightning as pl
-
 import numpy as np
-from data_loader.pytorch_dataset import get_dataloader
 
 class Model(ABC):
 
@@ -39,68 +34,6 @@ class Model(ABC):
     def initialize_network(self, input_dim:int, output_dim:int):
         pass
 
-class SKLearnModel(Model):
 
-    data_scaling = 'scaled'
-    only_column = None
-    feature_selection = 'on'
-    model_type = 'ml'
-    predict_window_size = 'single_timestamp'
-
-    def __init__(self, model):
-        self.model = model
-    
-    def fit(self, X: np.ndarray, y: np.ndarray) -> None:
-        self.model.fit(X, y)
-
-    def predict(self, X) -> tuple[float, np.ndarray]:
-        pred = self.model.predict(X).item()
-        probability = self.model.predict_proba(X).squeeze()
-        return (pred, probability)
-    
-    def clone(self) -> SKLearnModel:
-        return SKLearnModel(clone(self.model))
-
-    def get_name(self) -> str:
-        return self.model.__class__.__name__
-    
-    def initialize_network(self, input_dim:int, output_dim:int):
-        pass
     
     
-    
-class LightningNeuralNetModel(Model):
-
-    data_scaling = 'scaled'
-    only_column = None
-    feature_selection = 'off'
-    model_type = 'ml'
-
-    ''' Standard lightning methods '''
-    
-    def __init__(self, model, max_epochs=5):
-        self.model = model
-        self.trainer = pl.Trainer(max_epochs=max_epochs)
-        
-    def fit(self, X: np.ndarray, y: np.ndarray) -> None:
-        train_dataloader = self.__prepare_data(X.astype(float), y.astype(float))
-        self.trainer.fit(self.model, train_dataloader)
-        
-    def predict(self, X: np.ndarray) -> tuple[float, np.ndarray]:
-        return self.model(X)
-
-    def clone(self):
-        model_copy = copy.deepcopy(self.model)
-        return LightningNeuralNetModel(model_copy)
-    
-    ''' Non-standard lightning methods '''
-    def __prepare_data(self, X:np.ndarray, y:np.ndarray):
-        dataloader = get_dataloader(X, y)
-        return dataloader
-
-    def initialize_network(self, input_dim:int, output_dim:int):
-        self.model.initialize_network(input_dim, output_dim)
-
-    def get_name(self) -> str:
-        return self.model.__class__.__name__
-