From 5a7aa7025cc3a8056e146e49eac89b0a5f925f65 Mon Sep 17 00:00:00 2001
From: jh-206 <jhirschi206@gmail.com>
Date: Mon, 23 Sep 2024 15:44:15 -0600
Subject: [PATCH] Update other ML models

---
 fmda/moisture_models.py | 10 ++++++++--
 fmda/params.yaml        |  6 ++++--
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/fmda/moisture_models.py b/fmda/moisture_models.py
index 32c0a2e..e8a7f32 100644
--- a/fmda/moisture_models.py
+++ b/fmda/moisture_models.py
@@ -2,6 +2,12 @@ import numpy as np
 import math
 import matplotlib.pyplot as plt
 import copy
+from abc import ABC, abstractmethod
+import xgboost as xg
+from xgboost import XGBRegressor
+from sklearn.metrics import mean_squared_error
+import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
 
 # ODE + Augmented Kalman Filter Code
 #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -214,9 +220,9 @@ class MLModel(ABC):
     def eval(self, X_test, y_test):
         preds = self.predict(X_test)
         rmse = np.sqrt(mean_squared_error(y_test, preds))
-        rmse_ros = np.sqrt(mean_squared_error(ros_3wind(y_test), ros_3wind(preds)))
+        # rmse_ros = np.sqrt(mean_squared_error(ros_3wind(y_test), ros_3wind(preds)))
         print(f"Test RMSE: {rmse}")
-        print(f"Test RMSE (ROS): {rmse_ros}")
+        # print(f"Test RMSE (ROS): {rmse_ros}")
         return rmse, rmse_ros
 
 class XGB(MLModel):
diff --git a/fmda/params.yaml b/fmda/params.yaml
index 34effd1..2aa308d 100644
--- a/fmda/params.yaml
+++ b/fmda/params.yaml
@@ -134,7 +134,8 @@ xgb:
   scale_pos_weight: 1
   n_estimators: 100
   gamma: .1
-  
+  scaler: 'standard'
+  features_list: ['Ed', 'Ew', 'solar', 'wind', 'rain']
 
   ### Params sent by Schreck, slow and less accurate for this dataset
     # objective: "reg:squarederror"
@@ -165,7 +166,8 @@ rf:
 
 lm:
   fit_intercept: true
-
+  scaler: null
+  features_list: ['Ed', 'Ew', 'solar', 'wind', 'rain']
 
 
 
-- 
2.11.4.GIT