diff --git a/DIARIO.txt b/DIARIO.txt
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index 0000000..3f70a72
--- /dev/null
+++ b/DIARIO.txt
@@ -0,0 +1,8 @@
+Problema: desbalanceo de resultados debido a muchos ceros en algunas variables
+
+1. Probamos sklearn.RF con dataset urineFlav con dropna, que es el que menos afectado se ve
+
+2. Probamos XGBRF para manejar NAs sin necesidad de imputarlos a cero. 
+
+Posibles soluciones: 
+Hay que ver si hay implementación de MLP que maneje NAs
\ No newline at end of file
diff --git a/models/RF_Full_5050urineFlav.pkl b/models/RF_Full_5050urineFlav.pkl
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index 0000000..112f775
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diff --git a/models/RF_Full_lightTrainplasmAnt.pkl b/models/RF_Full_lightTrainplasmAnt.pkl
index 47bf86a..da01829 100644
Binary files a/models/RF_Full_lightTrainplasmAnt.pkl and b/models/RF_Full_lightTrainplasmAnt.pkl differ
diff --git a/models/pruebaRF b/models/pruebaRF
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index 0000000..7561079
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diff --git a/models/pruebaRF.json b/models/pruebaRF.json
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index 0000000..904caed
--- /dev/null
+++ b/models/pruebaRF.json
@@ -0,0 +1 @@
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diff --git a/pendientes.md b/pendientes.md
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--- /dev/null
+++ b/pendientes.md
@@ -0,0 +1,10 @@
+# Pendientes
+- ajustar nombre modelo -> refinar
+- probar 50/50, 60/40 train
+- modulos evalScore, plots, RF_fit, XGB_fit, NN_fit (quizá NN_eval)
+- gestionar dependencias
+
+## ideas NAs
+- usar los voluntarios con ceros como training
+- probar todas las posibles combinaciones de retirar NAs en train
+
diff --git a/recursosMetab.md b/recursosMetab.md
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--- /dev/null
+++ b/recursosMetab.md
@@ -0,0 +1,14 @@
+# Recursos metabolómica
+
+## General
+- http://internationalmetabolomicssociety.org/resources
+
+## DDBB
+- https://mona.fiehnlab.ucdavis.edu/
+- http://bigg.ucsd.edu/
+- https://bmrb.io/metabolomics/
+- https://www.ebi.ac.uk/metabolights
+
+## DDBB adicionales
+- https://www.genome.jp/kegg/pathway.html
+- 
\ No newline at end of file
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diff --git a/results/predicts/.ipynb_checkpoints/scores_pruebaRF-checkpoint.csv b/results/predicts/.ipynb_checkpoints/scores_pruebaRF-checkpoint.csv
new file mode 100644
index 0000000..9aba110
--- /dev/null
+++ b/results/predicts/.ipynb_checkpoints/scores_pruebaRF-checkpoint.csv
@@ -0,0 +1,111 @@
+,Metabolite modeled,MAE score,Mean MAE score
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diff --git a/results/predicts/df_pred-test_RF_Full_5050urineFlav.csv b/results/predicts/df_pred-test_RF_Full_5050urineFlav.csv
new file mode 100644
index 0000000..9edcb85
--- /dev/null
+++ b/results/predicts/df_pred-test_RF_Full_5050urineFlav.csv
@@ -0,0 +1,101 @@
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diff --git a/results/predicts/df_pred-test_RF_Full_lightTrainplasmAnt.csv b/results/predicts/df_pred-test_RF_Full_lightTrainplasmAnt.csv
new file mode 100644
index 0000000..0f481ba
--- /dev/null
+++ b/results/predicts/df_pred-test_RF_Full_lightTrainplasmAnt.csv
@@ -0,0 +1,62 @@
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diff --git a/scripts/df_pred-test_plasmAnt.csv b/results/predicts/df_pred-test_plasmAnt.csv
similarity index 100%
rename from scripts/df_pred-test_plasmAnt.csv
rename to results/predicts/df_pred-test_plasmAnt.csv
diff --git a/results/predicts/df_pred-test_pruebaRF.csv b/results/predicts/df_pred-test_pruebaRF.csv
new file mode 100644
index 0000000..f7b0b1a
--- /dev/null
+++ b/results/predicts/df_pred-test_pruebaRF.csv
@@ -0,0 +1,26 @@
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diff --git a/results/predicts/scores_RF_Full_5050urineFlav.csv b/results/predicts/scores_RF_Full_5050urineFlav.csv
new file mode 100644
index 0000000..456bab3
--- /dev/null
+++ b/results/predicts/scores_RF_Full_5050urineFlav.csv
@@ -0,0 +1,106 @@
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diff --git a/results/predicts/scores_RF_Full_lightTrainplasmAnt.csv b/results/predicts/scores_RF_Full_lightTrainplasmAnt.csv
new file mode 100644
index 0000000..5d31392
--- /dev/null
+++ b/results/predicts/scores_RF_Full_lightTrainplasmAnt.csv
@@ -0,0 +1,136 @@
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diff --git a/results/predicts/scores_plasmAnt.csv b/results/predicts/scores_plasmAnt.csv
new file mode 100644
index 0000000..1ef2be8
--- /dev/null
+++ b/results/predicts/scores_plasmAnt.csv
@@ -0,0 +1,136 @@
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diff --git a/results/predicts/scores_pruebaRF.csv b/results/predicts/scores_pruebaRF.csv
new file mode 100644
index 0000000..b99053b
--- /dev/null
+++ b/results/predicts/scores_pruebaRF.csv
@@ -0,0 +1,146 @@
+,Metabolite modeled,MAE score,Mean MAE score
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+  8.38729095 10.26163101  2.58573914  9.7081871   3.83745956  5.127388  ]",6.605591371324326
+26,Bpmax,"[17.52255503  4.14955393  9.31524404  8.15588633  1.87963613  5.85207367
+ 10.47616959  9.0378418   6.61186981 11.54936981  8.90685527  9.08938853
+ 12.18299103  4.69315084 11.17108409  8.16268539  8.20730591  4.5027771
+ 14.62401199  6.53424072  5.41768901  9.61383057  9.29954783  6.47935232
+ 15.39959717 10.74590302  4.85251617  7.00317383 10.66070175 11.29212952]",8.779637739393445
+27,Frec,"[19.64086787 18.16724014 24.84482829 12.43347931  8.56687419 41.21522141
+ 42.51914978  9.45299911  6.06136131 18.07376862 14.79331843 27.17759705
+ 16.9820315  12.94936117  4.14396159 27.6782589  41.85367966  4.91309738
+  9.33852196 20.04413986  9.91543325  7.35836665 33.29686483 34.79421997
+ 15.7885526  12.88799286 18.61943626 11.82810593 16.62627602  7.80026245]",18.325508944193523
+28,,"[2.21358257 1.7398061  2.09650064 1.73184575 1.0127822  2.59154713
+ 2.91902013 1.61848925 1.36314525 1.87538927 1.69695622 2.21491957
+ 2.08394626 1.34263205 1.57452618 2.04034136 2.94193687 1.46284551
+ 1.52480295 1.57725239 1.26880587 1.54191138 2.43723856 2.2067072
+ 1.98058879 2.08155309 1.90894457 1.61322421 1.71692927 1.68258202]",1.86869175338274
diff --git a/scripts/.ipynb_checkpoints/fitEnsemble-checkpoint.py b/scripts/.ipynb_checkpoints/fitEnsemble-checkpoint.py
new file mode 100644
index 0000000..3a5cc6b
--- /dev/null
+++ b/scripts/.ipynb_checkpoints/fitEnsemble-checkpoint.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+"""
+@author: dres2
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pickle
+import re
+import os
+from utils import encodingSplitting
+from sklearn.model_selection import RepeatedKFold, cross_val_score, GridSearchCV
+from xgboost import XGBRFRegressor
+
+paths = ["plasmFlav_ord.csv", "plasmAnt_ord.csv", "urineFlav_ord.csv", "urineAnt_ord.csv"]
+paths = ["../data/" + s for s in paths]
+
+def fitRF():
+    '''
+    function to train a Random Forest 
+    '''
+    
+    print("lalala")
+
+fitRF()
\ No newline at end of file
diff --git a/scripts/.ipynb_checkpoints/main_retrain-checkpoint.py b/scripts/.ipynb_checkpoints/main_retrain-checkpoint.py
new file mode 100644
index 0000000..f66c230
--- /dev/null
+++ b/scripts/.ipynb_checkpoints/main_retrain-checkpoint.py
@@ -0,0 +1,203 @@
+# packages
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pickle
+import re
+import os
+from utils import encodingSplitting
+from sklearn.model_selection import RepeatedKFold, cross_val_score, GridSearchCV
+#from xgboost import XGBRegressor
+from sklearn.ensemble import RandomForestRegressor
+
+sns.set_theme()
+
+def evaluateNPlot(modelPath, df, df_name, y_test ='' , X_test='', full = True,  pipeline = True,  plot = True):
+   
+   # extract model name  
+    
+    modelname = re.sub(".pkl","",re.sub("../models/", "", modelPath)) # +(RF|XGB|MLP)+(Met_lightTrain|Full_lightTrain)","", modelPath))
+
+    # define model type
+    if re.match("Full", modelPath):
+        full = True
+
+    # open the model
+
+    with open (modelPath, 'rb') as f:
+        model = pickle.load(f)
+    
+    print(" ----------------- MODEL EXPORTED ----------------- ")
+    print(" ----------------- EVALUATION STARTING ----------------- ")
+
+    # define the evaluation procedure
+    
+    cv = RepeatedKFold(n_splits=3, n_repeats=3, random_state=42)
+    
+    # read or inherit data
+    
+    if (not pipeline):
+        X_train, X_test, y_train, y_test = encodingSplitting(df, full = full)
+     
+    # for full models
+    
+    if (full):   
+        print(" ----------------- ANTROPOMETRIC + METABOLIC MODEL EVALUATION STARTING ----------------- ")
+    else:  
+        print(" ----------------- ONLY METABOLIC MODEL EVALUATION STARTING ----------------- ")
+        
+    #X_test.to_csv("X_test_"+modelname+".csv") 
+    
+    scores = []
+    mean_scores = []
+    metabs = pd.Series(y_test.columns)
+    
+    for n in metabs:
+        
+        print(" ----------------- " + n + " MODEL EVALUATION ----------------- ")
+
+        n_scores = cross_val_score(model, X_test, y_test[n], scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
+        n_scores = np.absolute(n_scores)
+        scores.append(n_scores) 
+        mean_scores.append(np.mean(n_scores))
+
+        print('Full model ' + modelname + ' for metabolite '+n+ ' MAE: %.3f (%.3f)' % (np.mean(n_scores), np.std(n_scores)))
+    
+    # full model evaluation
+    print(" ----------------- MEAN PERFORMANCE EVALUATION ----------------- ")
+
+    n_score_all = cross_val_score(model, X_test, y_test, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
+    n_score_all = np.absolute(n_score_all)
+    scores.append((n_score_all))
+    mean_scores.append(np.mean(n_score_all))
+    df_scores = pd.concat([metabs, pd.Series(scores), pd.Series(mean_scores)], axis = 1, ignore_index = True)
+    df_scores.columns = ["Metabolite modeled", "MAE score", "Mean MAE score"]
+    df_scores.to_csv("../results/predicts/scores_" + modelname +".csv", sep = ",")
+    
+    print("Mean performance of all model" + modelname +  " MAE: %.3f (%.3f)" % (np.mean(n_score_all), np.std(n_score_all)))
+    
+    # plotting
+    if (plot):
+    
+        print(" ----------------- PLOTTING STARTING ----------------- ")
+
+        # inherit model, modelname, df, df_name, testSplits
+        print(" ----------------- REBUILDING CATEGORICAL VARIABLES----------------- ")
+
+        dummies_sweetener = pd.from_dummies(X_test[['Sweetener_SA', 'Sweetener_ST','Sweetener_SU']]).replace("Sweetener_", "",regex=True)
+        dummies_sex = pd.from_dummies(X_test[['Sex_MAN', "Sex_WOMAN"]]).replace("Sex_", "",regex=True)
+
+        print(" ----------------- PREDICTING AND JOINING PRED + TEST ----------------- ")
+
+        # make predict
+        y_pred = model.predict(X_test)
+        # pred_prueba_DF = pd.DataFrame(pred_prueba, index = X_test.index-1, columns=y_test.columns).add_suffix("_pred").join(y_test.add_suffix('_test')).fillna(0) 
+        df_predTest = pd.DataFrame([i for i in y_pred], index = y_test.index, columns= y_test.columns).add_suffix("_pred").join(y_test.add_suffix('_test')).fillna(0)
+        df_predTest["Sex"] = dummies_sex
+        df_predTest["Sweetener"] = dummies_sweetener
+        df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv("../results/predicts/df_pred-test_"+modelname+".csv", sep = ",")
+            
+        # print plots
+        metabs = y_test.columns.drop(list(y_test.filter(regex='Sex|Sweetener'))) 
+
+        print(" ----------------- STARTING PLOTTING ----------------- ")
+	
+    	
+        for metab in metabs:
+            #sns.regplot(data = pred_prueba_DF, x=metab+"_pred", y=metab+"_test", )
+            
+            print(" ----------------- PREDICTED VS TEST " + metab + " ----------------- ")
+
+            
+
+            adjustAxis = True
+            if (adjustAxis):
+                ax = plt.gca()
+                minPred = min(df_predTest[metab+"_pred"])
+                maxPred = max(df_predTest[metab+"_pred"])
+                minTest = min(df_predTest[metab+"_test"])
+                maxTest = max(df_predTest[metab+"_test"])
+                ax.set_xlim([min(minPred,  minTest),  max(maxPred,  maxTest)])
+                ax.set_ylim([min(minPred,  minTest),  max(maxPred,  maxTest)])
+
+            
+            plt.figure(figsize = (10,10))
+            sns.lmplot(data = df_predTest, x=metab+"_pred", y=metab+"_test")
+            plt.title("TEST VS PREDICTED lmplot " + metab + " " + modelname)
+            plt.savefig("../results/plots/testVsPredicted_lmplot_"+ metab+"_"+modelname+".png")
+            plt.close("all")   
+
+            print(" ----------------- PREDICTED VS RESIDUALS " + metab + " ----------------- ")
+
+            plt.figure(figsize = (10,10))
+            y_pred_metab = df_predTest[metab+"_pred"]
+            residuals = df_predTest[metab+"_test"]-df_predTest[metab+"_pred"]
+            df_all = pd.concat([y_pred_metab, residuals], axis=1)
+            df_all.columns = ["Predicted", "Residuals"]
+            
+            sns.scatterplot(data = df_all, x="Predicted", y="Residuals")
+            plt.title("RESIDUALS VS PREDICTED " + metab + " " + modelname)
+            plt.axhline(y=0)
+            plt.savefig("../results/plots/residualsVsPredicted_"+ metab+"_"+modelname+".png")
+            plt.close("all")   
+
+    
+def RF_Fit(df, df_name, full = True, eval = True,  plot = True):
+    
+    
+    param_grid_extensive = {
+    'bootstrap': [False, True],
+    'max_depth': [1, 10, 20, 30, 50, 70, 100, None],
+    'max_features': ["auto", "sqrt", "log2", 0.2, 0.5, 0.7, 0.9],
+    'min_samples_leaf': [1, 2, 3, 4, 5, 8, 10, 12, 20, 30],
+    'min_samples_split': [2, 3, 4, 5, 8, 10, 12, 20, 30],
+    'n_estimators': [10, 50, 100, 200, 300, 500, 700, 1000]
+    }
+    
+    param_grid_light = {
+    'bootstrap': [False, True],
+    'max_depth': [80, 90, 100, 110],
+    'max_features': [0.2, 0.5, 0.7, 0.9, 2, 3],
+    'min_samples_leaf': [3, 4, 5],
+    'min_samples_split': [8, 10, 12],
+    'n_estimators': [100, 200, 300, 1000]
+    }
+    
+
+    print(" ----------------- SETTING UP TRAINING ----------------- ")
+    
+    X_train, X_test, y_train, y_test = encodingSplitting(df, full = full)
+    
+    model = RandomForestRegressor()
+    
+    cv = RepeatedKFold(n_splits=3, n_repeats=3, random_state=42)
+    
+    grid_search = GridSearchCV(estimator = model, param_grid = param_grid_light, cv = cv, n_jobs = -1, verbose = 2)
+
+    print(" ----------------- STARTING TRAINING ----------------- ")
+
+    grid_search.fit(X_train, y_train)
+
+    print(" ----------------- TRAINING ENDED ----------------- ")
+
+    if (full):    
+        filename = os.path.join("..", "models","RF_Full_5050"+df_name+".pkl")
+                
+    else: 
+        filename = os.path.join("..", "models","RF_Met_5050"+df_name+".pkl")
+    
+    best_model = grid_search.best_estimator_
+    
+    with open(filename, 'wb') as file:
+        pickle.dump(best_model, file)
+    
+    print(" ----------------- MODEL SAVED ----------------- ")
+
+    if (eval):
+        evaluateNPlot(filename, df, df_name, y_test=y_test, X_test=X_test, full = full,  pipeline = True,  plot = plot)
+    else:
+        print(" ----------------- ENDED SCRIPT ----------------- ")
+
+
diff --git a/scripts/.ipynb_checkpoints/modelSanoApp-checkpoint.py b/scripts/.ipynb_checkpoints/modelSanoApp-checkpoint.py
new file mode 100644
index 0000000..3253e1e
--- /dev/null
+++ b/scripts/.ipynb_checkpoints/modelSanoApp-checkpoint.py
@@ -0,0 +1,65 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May  8 21:27:35 2024
+
+@author: dres2
+"""
+
+from main_retrain import evaluateNPlot, RF_Fit
+from utils import fullRead
+# from fitEnsemble import fitRF
+import re
+import pandas as pd
+import sys
+
+testeo = sys.argv[1]
+ejecucion = sys.argv[2]
+ejecucionPeque = sys.argv[3]
+
+
+print(" ----------------- PACKAGES EXPORTING ENDED ----------------- ")
+
+print(" ----------------- READING DATASETS ----------------- ")
+
+paths = ["plasmFlav_ord.csv", "plasmAnt_ord.csv", "urineFlav_ord.csv", "urineAnt_ord.csv"]
+paths = ["../data/" + s for s in paths]
+
+
+# execution
+df, df_name = fullRead(paths[2], sep = ",", full = True)
+df_name = re.sub("../", "", df_name)
+df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric)
+RF_Fit(df, df_name, full = True, eval=True,  plot = True)
+
+if testeo == True:
+
+    print(" ----------------- TEST MODE INCIATING ----------------- ")
+
+    df, df_name = fullRead(paths[1], sep = ",", full = True)
+    df_name = re.sub("../", "", df_name)
+    df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric)
+    
+    #y_test = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/X_metTest_RF_Met_lightTrainplasmFlav.csv") 
+    #X_test = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/y_metTest_RF_Met_lightTrainplasmFlav.csv")
+    
+    residPlot = False  
+    evaluateNPlot(modelPath = "../models/RF_Full_lightTrainplasmAnt.pkl", df = df,  df_name = df_name,  pipeline = False)
+
+else:
+    print(" ----------------- NO TESTING MODE ----------------- ")
+
+if ejecucion == True: 
+    
+    print(" ----------------- EXECUTION MODE INCIATING ----------------- ")
+    
+    for path in paths:
+        df, df_name = fullRead(path, sep = ",", full = True)
+        df_name = re.sub("../", "", df_name)
+        df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric)
+
+        RF_Fit(df, df_name, full = True, eval=True,  plot = True)
+        RF_Fit(df, df_name, full = False, eval=True,  plot = True)
+else:
+    print(" ----------------- NO EXECUTION MODE ----------------- ")
+
+    
diff --git a/scripts/.ipynb_checkpoints/pruebas-checkpoint.ipynb b/scripts/.ipynb_checkpoints/pruebas-checkpoint.ipynb
index 8facc90..682b4d9 100644
--- a/scripts/.ipynb_checkpoints/pruebas-checkpoint.ipynb
+++ b/scripts/.ipynb_checkpoints/pruebas-checkpoint.ipynb
@@ -2,1104 +2,1114 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "2aff3527-b25f-4f6c-b4f7-44109012489f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import re \n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def fullRead(pathToTable, sep, full = False):\n",
-    "\n",
-    "  df_renamed = pd.read_csv(pathToTable, sep = sep, encoding = \"latin_1\")\n",
-    "    \n",
-    "  df_name = re.sub(\"_ord.csv\",\"\",(re.sub(\"data/\", \"\" ,pathToTable)))\n",
-    "  \n",
-    "\n",
-    "  print (\"Reading dataset \" + df_name)\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")\n",
-    "  \n",
-    "  # reading and merging    \n",
-    "  \n",
-    "  if (full):\n",
-    "    print (\"Reading and merging anthro data\")\n",
-    "    print (\"#\")\n",
-    "    print (\"#\")\n",
-    "    print (\"#\")  \n",
-    "   \n",
-    "    df_anthro = pd.read_csv( os.path.join(\"..\",\"data\",\"chronicAnthropometricCardiovascularData.csv\"), sep=\";\", decimal=\",\")\n",
-    "    df_renamed = df_renamed.merge(df_anthro)\n",
-    "\n",
-    "    # separating by time moment and renaming\n",
-    "\n",
-    "    df_renamed[\"Weight\"] = \"\"\n",
-    "    df_renamed[\"BMI\"] = \"\"\n",
-    "    df_renamed[\"Fat\"] = \"\"\n",
-    "    df_renamed[\"CVRI\"] = \"\"\n",
-    "    df_renamed[\"Bpmin\"] = \"\"\n",
-    "    df_renamed[\"Bpmax\"] = \"\"\n",
-    "    df_renamed[\"Frec\"] = \"\"\n",
-    "\n",
-    "    for i in range(len(df_renamed)):\n",
-    "\n",
-    "        if df_renamed.loc[i][\"Time\"] == \"Initial\":\n",
-    "            df_renamed.loc[i,\"Weight\"] = df_renamed.loc[i][\"Peso inicial\"]\n",
-    "            df_renamed.loc[i,\"BMI\"] = df_renamed.loc[i][\"IMC Inicial\"]\n",
-    "            df_renamed.loc[i,\"Fat\"] = df_renamed.loc[i][\"Grasa inicial\"]\n",
-    "            df_renamed.loc[i,\"CVRI\"] = df_renamed.loc[i][\"IRCV inicial\"] \n",
-    "            df_renamed.loc[i,\"Bpmin\"] = df_renamed.loc[i][\"Bpmin inicial\"] \n",
-    "            df_renamed.loc[i,\"Bpmax\"] = df_renamed.loc[i][\"Bpmax inicial\"] \n",
-    "            df_renamed.loc[i,\"Frec\"] = df_renamed.loc[i][\"Frec inicial\"] \n",
-    "                \n",
-    "        if df_renamed.loc[i][\"Time\"] == \"Final\":\n",
-    "        \n",
-    "            df_renamed.loc[i,\"Weight\"] = df_renamed.loc[i][\"Peso final\"]\n",
-    "            df_renamed.loc[i,\"BMI\"] = df_renamed.loc[i][\"IMC Final\"]\n",
-    "            df_renamed.loc[i,\"Fat\"] = df_renamed.loc[i][\"Grasa final\"]\n",
-    "            df_renamed.loc[i,\"CVRI\"] = df_renamed.loc[i][\"IRCV Final\"] \n",
-    "            df_renamed.loc[i,\"Bpmin\"] = df_renamed.loc[i][\"Bpmin final\"] \n",
-    "            df_renamed.loc[i,\"Bpmax\"] = df_renamed.loc[i][\"Bpmax final\"] \n",
-    "            df_renamed.loc[i,\"Frec\"] = df_renamed.loc[i][\"Frec final\"] \n",
-    "        \n",
-    "    df_renamed.drop(columns = [\"Peso inicial\", \"Peso final\", \"Delta Peso\", \"Talla\", \"IMC Inicial\", \"IMC Final\", \"Delta IMC\", \"Grasa inicial\", \"Grasa final\", \"Delta Grasa\", \"IRCV Final\", \"IRCV inicial\", \"Bpmin final\", \"Bpmin inicial\", \"Bpmax final\", \"Bpmax inicial\", \"Frec final\", \"Frec inicial\",], inplace=True )\n",
-    "  \n",
-    "  #   \n",
-    "  df_renamed.drop(columns = [\"Unnamed: 0\", \"grouping\"], inplace=True )\n",
-    "  \n",
-    "  print (\"Filling NA with zeros\")\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")  \n",
-    "  df_renamed.fillna(0, inplace=True)\n",
-    "  \n",
-    "  return (df_renamed, df_name)\n",
-    "\n",
-    "def scaling(df_read):\n",
-    "   \n",
-    "   import numpy as np\n",
-    "   from sklearn import preprocessing\n",
-    "\n",
-    "   scaler = preprocessing.RobustScaler()\n",
-    "   numCols = df_read.select_dtypes(include=np.number).drop(\"numVol\", axis=1).columns\n",
-    "   df_read[numCols] = scaler.fit_transform(df_read[numCols])\n",
-    "   \n",
-    "   return (df_read)\n",
-    "\n",
-    "def encodingSplitting(df, full = True):\n",
-    "    \n",
-    "    from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "    \n",
-    "    df = pd.get_dummies(df, columns = [\"Sweetener\", \"Sex\", \"Time\"], drop_first=False)\n",
-    "    \n",
-    "    if (full):\n",
-    "        print (\"Split for anthropometric + metabolic\")\n",
-    "\n",
-    "        X, y= df[df[\"Time_Initial\"] == 1].set_index(\"numVol\").drop([\"Time_Initial\", \"Time_Final\"], axis=1), df[df[\"Time_Final\"] == 1].set_index(\"numVol\").drop(['Sweetener_SA', 'Sweetener_ST','Sweetener_SU','Time_Final', 'Time_Initial', 'Sex_MAN', 'Sex_WOMAN'], axis = 1)\n",
-    "        \n",
-    "    else:\n",
-    "        print (\"Split for only metabolic\")\n",
-    "        X, y = df[df[\"Time_Initial\"] == 1].set_index(\"numVol\").drop([\"Time_Initial\", \"Time_Final\"], axis=1), df[df[\"Time_Final\"] == 1].set_index(\"numVol\").drop(['Sweetener_SA', 'Sweetener_ST','Sweetener_SU', 'Sex_MAN', 'Sex_WOMAN', 'Time_Final', 'Time_Initial', 'Weight','BMI', 'Fat', 'CVRI', 'Bpmin', 'Bpmax', 'Frec'], axis = 1)\n",
-    "        \n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "    \n",
-    "\n",
-    "    return(X_train, X_test, y_train, y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "c3f8398f-1334-423b-b76a-80e9a1139e12",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['.git', '.ipynb_checkpoints', 'data', 'results', 'scripts']"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.listdir(\"../\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "f1563bc1-b29e-4838-ab88-0eab438f3d90",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 1,
+   "id": "0132bde1-6762-48e0-a2bf-73531f9cc934",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reading dataset ..\\data\\plasmAnt\n",
+      "XGBoost version: 2.0.3\n",
+      "LightGBM version: 4.3.0\n",
+      "Optuna version: 3.6.1\n",
+      "Scikit-learn version: 1.4.2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/die/eric_env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Tratamiento de datos\n",
+    "# ==============================================================================\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from utils import fullRead, scaling\n",
+    "# Gráficos\n",
+    "# ==============================================================================\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "# Modelado\n",
+    "# ==============================================================================\n",
+    "import xgboost\n",
+    "from xgboost import XGBRFRegressor\n",
+    "import lightgbm\n",
+    "from lightgbm import LGBMRegressor\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split, RepeatedKFold, cross_val_score\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.preprocessing import TargetEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "import sklearn\n",
+    "import optuna\n",
+    "import time\n",
+    "\n",
+    "# Configuración warnings\n",
+    "# ==============================================================================\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+    "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
+    "\n",
+    "print(f\"XGBoost version: {xgboost.__version__}\")\n",
+    "print(f\"LightGBM version: {lightgbm.__version__}\")\n",
+    "print(f\"Optuna version: {optuna.__version__}\")\n",
+    "print(f\"Scikit-learn version: {sklearn.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e30c566c-2fe7-4cff-bbfd-975b48611e95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading dataset urineAnt\n",
       "#\n",
       "#\n",
       "#\n",
       "#\n",
-      "Reading and merging anthro data\n",
-      "#\n",
-      "#\n",
-      "#\n",
       "Filling NA with zeros\n",
       "#\n",
       "#\n",
       "#\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(Index([ 19,  54,  56, 108,   5,  41,  72, 128,  32,  64,  62, 140,  11, 109,\n",
-       "        130,  12,  86,  65, 136,   1,  27,  53,  76, 117,  25],\n",
-       "       dtype='int64', name='numVol'),\n",
-       " Index([ 19,  54,  56, 108,   5,  41,  72, 128,  32,  64,  62, 140,  11, 109,\n",
-       "        130,  12,  86,  65, 136,   1,  27,  53,  76, 117,  25],\n",
-       "       dtype='int64', name='numVol'),\n",
-       " Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
-       "        ...\n",
-       "        133, 134, 135, 136, 137, 138, 139, 140, 141, 142],\n",
-       "       dtype='int64', name='numVol', length=122),\n",
-       " Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
-       "        ...\n",
-       "        133, 134, 135, 136, 137, 138, 139, 140, 141, 142],\n",
-       "       dtype='int64', name='numVol', length=122))"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "import os\n",
-    "from sklearn.model_selection import train_test_split\n",
+    "paths = [\"plasmFlav_ord.csv\", \"plasmAnt_ord.csv\", \"urineFlav_ord.csv\", \"urineAnt_ord.csv\"]\n",
+    "paths = [\"../data/\" + s for s in paths]\n",
     "\n",
-    "pathPlasmAnt = os.path.join(\"..\",\"data\", \"plasmAnt_ord.csv\")\n",
-    "df, df_name = fullRead(pathPlasmAnt, sep = \",\", full = True)\n",
-    "df = pd.get_dummies(df, columns = [\"Sweetener\", \"Sex\", \"Time\"], drop_first=False)\n",
-    "\n",
-    "X, y= df[df[\"Time_Initial\"] == 1].set_index(\"numVol\").drop([\"Time_Initial\", \"Time_Final\"], axis=1), df[df[\"Time_Final\"] == 1].set_index(\"numVol\").drop(['Sweetener_SA', 'Sweetener_ST','Sweetener_SU','Time_Final', 'Time_Initial', 'Sex_MAN', 'Sex_WOMAN'], axis = 1)\n",
-    "\n",
-    "# X_train, X_test, y_train, y_test = encodingSplitting(df, full = True)\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "X_test.index,y_test.index, X.index, y.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "4c708140-7b74-486c-afa8-7aad8470a3e3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CA</th>\n",
-       "      <th>CA.G</th>\n",
-       "      <th>CA.S</th>\n",
-       "      <th>Total.CA</th>\n",
-       "      <th>DHPAA</th>\n",
-       "      <th>DHPAA.G</th>\n",
-       "      <th>DHPAA.GG</th>\n",
-       "      <th>DHPAA.GS</th>\n",
-       "      <th>DHPAA.SS</th>\n",
-       "      <th>Total.DHPAA</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Fat</th>\n",
-       "      <th>CVRI</th>\n",
-       "      <th>Bpmin</th>\n",
-       "      <th>Bpmax</th>\n",
-       "      <th>Frec</th>\n",
-       "      <th>Sweetener_SA</th>\n",
-       "      <th>Sweetener_ST</th>\n",
-       "      <th>Sweetener_SU</th>\n",
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-       "      <th>Sex_WOMAN</th>\n",
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-       "      <th>numVol</th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.036145</td>\n",
-       "      <td>0.117857</td>\n",
-       "      <td>0.241935</td>\n",
-       "      <td>0.173442</td>\n",
-       "      <td>0.273438</td>\n",
-       "      <td>0.098739</td>\n",
-       "      <td>0.017949</td>\n",
-       "      <td>0.301020</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.187515</td>\n",
-       "      <td>...</td>\n",
-       "      <td>44.7</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>137.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.067857</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.032520</td>\n",
-       "      <td>0.253125</td>\n",
-       "      <td>0.132353</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.520408</td>\n",
-       "      <td>0.052764</td>\n",
-       "      <td>0.197335</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.6</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.433735</td>\n",
-       "      <td>0.089286</td>\n",
-       "      <td>0.080645</td>\n",
-       "      <td>0.214092</td>\n",
-       "      <td>0.302187</td>\n",
-       "      <td>0.131303</td>\n",
-       "      <td>0.010897</td>\n",
-       "      <td>0.188776</td>\n",
-       "      <td>0.118090</td>\n",
-       "      <td>0.212532</td>\n",
-       "      <td>...</td>\n",
-       "      <td>39.2</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.144578</td>\n",
-       "      <td>0.082143</td>\n",
-       "      <td>0.145161</td>\n",
-       "      <td>0.154472</td>\n",
-       "      <td>0.247812</td>\n",
-       "      <td>0.018908</td>\n",
-       "      <td>0.028846</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.246231</td>\n",
-       "      <td>0.148001</td>\n",
-       "      <td>...</td>\n",
-       "      <td>41.2</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>73.0</td>\n",
-       "      <td>143.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.048193</td>\n",
-       "      <td>0.057143</td>\n",
-       "      <td>0.193548</td>\n",
-       "      <td>0.121951</td>\n",
-       "      <td>0.204687</td>\n",
-       "      <td>0.123950</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.017857</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.110124</td>\n",
-       "      <td>...</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>0.168675</td>\n",
-       "      <td>0.017857</td>\n",
-       "      <td>0.677419</td>\n",
-       "      <td>0.200542</td>\n",
-       "      <td>0.193125</td>\n",
-       "      <td>0.067227</td>\n",
-       "      <td>0.023077</td>\n",
-       "      <td>0.084184</td>\n",
-       "      <td>0.449749</td>\n",
-       "      <td>0.142390</td>\n",
-       "      <td>...</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>139</th>\n",
-       "      <td>0.024096</td>\n",
-       "      <td>0.050000</td>\n",
-       "      <td>0.064516</td>\n",
-       "      <td>0.089431</td>\n",
-       "      <td>0.140313</td>\n",
-       "      <td>0.047269</td>\n",
-       "      <td>0.097436</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.168342</td>\n",
-       "      <td>0.091653</td>\n",
-       "      <td>...</td>\n",
-       "      <td>23.8</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>102.0</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>0.204819</td>\n",
-       "      <td>0.014286</td>\n",
-       "      <td>0.080645</td>\n",
-       "      <td>0.105691</td>\n",
-       "      <td>0.285000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.171795</td>\n",
-       "      <td>0.163265</td>\n",
-       "      <td>0.118090</td>\n",
-       "      <td>0.228665</td>\n",
-       "      <td>...</td>\n",
-       "      <td>23.6</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>117.0</td>\n",
-       "      <td>61.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>141</th>\n",
-       "      <td>0.120482</td>\n",
-       "      <td>0.067857</td>\n",
-       "      <td>0.193548</td>\n",
-       "      <td>0.146341</td>\n",
-       "      <td>0.274375</td>\n",
-       "      <td>0.047269</td>\n",
-       "      <td>0.050641</td>\n",
-       "      <td>0.132653</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.173252</td>\n",
-       "      <td>...</td>\n",
-       "      <td>23.6</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>122.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>0.277108</td>\n",
-       "      <td>0.007143</td>\n",
-       "      <td>0.161290</td>\n",
-       "      <td>0.130081</td>\n",
-       "      <td>0.190625</td>\n",
-       "      <td>0.177521</td>\n",
-       "      <td>0.108333</td>\n",
-       "      <td>0.107143</td>\n",
-       "      <td>0.263819</td>\n",
-       "      <td>0.180968</td>\n",
-       "      <td>...</td>\n",
-       "      <td>21.9</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>121.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>122 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              CA      CA.G      CA.S  Total.CA     DHPAA   DHPAA.G  DHPAA.GG  \\\n",
-       "numVol                                                                         \n",
-       "1       0.036145  0.117857  0.241935  0.173442  0.273438  0.098739  0.017949   \n",
-       "2       0.000000  0.067857  0.000000  0.032520  0.253125  0.132353  0.000000   \n",
-       "3       0.433735  0.089286  0.080645  0.214092  0.302187  0.131303  0.010897   \n",
-       "4       0.144578  0.082143  0.145161  0.154472  0.247812  0.018908  0.028846   \n",
-       "5       0.048193  0.057143  0.193548  0.121951  0.204687  0.123950  0.000000   \n",
-       "...          ...       ...       ...       ...       ...       ...       ...   \n",
-       "138     0.168675  0.017857  0.677419  0.200542  0.193125  0.067227  0.023077   \n",
-       "139     0.024096  0.050000  0.064516  0.089431  0.140313  0.047269  0.097436   \n",
-       "140     0.204819  0.014286  0.080645  0.105691  0.285000  0.000000  0.171795   \n",
-       "141     0.120482  0.067857  0.193548  0.146341  0.274375  0.047269  0.050641   \n",
-       "142     0.277108  0.007143  0.161290  0.130081  0.190625  0.177521  0.108333   \n",
-       "\n",
-       "        DHPAA.GS  DHPAA.SS  Total.DHPAA  ...   Fat  CVRI  Bpmin  Bpmax  Frec  \\\n",
-       "numVol                                   ...                                   \n",
-       "1       0.301020  0.000000     0.187515  ...  44.7   8.0   86.0  137.0  70.0   \n",
-       "2       0.520408  0.052764     0.197335  ...  38.6   8.0   81.0  107.0  69.0   \n",
-       "3       0.188776  0.118090     0.212532  ...  39.2  11.0   75.0  107.0  64.0   \n",
-       "4       0.000000  0.246231     0.148001  ...  41.2   8.0   73.0  143.0  90.0   \n",
-       "5       0.017857  0.000000     0.110124  ...  34.0   6.0   61.0  100.0  74.0   \n",
-       "...          ...       ...          ...  ...   ...   ...    ...    ...   ...   \n",
-       "138     0.084184  0.449749     0.142390  ...  32.0  14.0   84.0  127.0  80.0   \n",
-       "139     0.000000  0.168342     0.091653  ...  23.8  13.0  102.0  148.0  67.0   \n",
-       "140     0.163265  0.118090     0.228665  ...  23.6   9.0   80.0  117.0  61.0   \n",
-       "141     0.132653  0.000000     0.173252  ...  23.6  13.0   80.0  122.0  67.0   \n",
-       "142     0.107143  0.263819     0.180968  ...  21.9   9.0   70.0  121.0  42.0   \n",
-       "\n",
-       "        Sweetener_SA  Sweetener_ST  Sweetener_SU  Sex_MAN Sex_WOMAN  \n",
-       "numVol                                                               \n",
-       "1              False          True         False    False      True  \n",
-       "2              False          True         False    False      True  \n",
-       "3              False          True         False    False      True  \n",
-       "4              False          True         False    False      True  \n",
-       "5              False          True         False    False      True  \n",
-       "...              ...           ...           ...      ...       ...  \n",
-       "138             True         False         False     True     False  \n",
-       "139             True         False         False     True     False  \n",
-       "140             True         False         False     True     False  \n",
-       "141             True         False         False     True     False  \n",
-       "142             True         False         False     True     False  \n",
-       "\n",
-       "[122 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "1ef5c169-c522-4e87-b6d3-944b2384888c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CA</th>\n",
-       "      <th>CA.G</th>\n",
-       "      <th>CA.S</th>\n",
-       "      <th>Total.CA</th>\n",
-       "      <th>DHPAA</th>\n",
-       "      <th>DHPAA.G</th>\n",
-       "      <th>DHPAA.GG</th>\n",
-       "      <th>DHPAA.GS</th>\n",
-       "      <th>DHPAA.SS</th>\n",
-       "      <th>Total.DHPAA</th>\n",
-       "      <th>...</th>\n",
-       "      <th>VA.GS</th>\n",
-       "      <th>VA.SS</th>\n",
-       "      <th>Total.VA</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>Fat</th>\n",
-       "      <th>CVRI</th>\n",
-       "      <th>Bpmin</th>\n",
-       "      <th>Bpmax</th>\n",
-       "      <th>Frec</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>numVol</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.602410</td>\n",
-       "      <td>0.046429</td>\n",
-       "      <td>0.629032</td>\n",
-       "      <td>0.311653</td>\n",
-       "      <td>0.105938</td>\n",
-       "      <td>0.105042</td>\n",
-       "      <td>0.063462</td>\n",
-       "      <td>0.165816</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.067805</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.088201</td>\n",
-       "      <td>0.256156</td>\n",
-       "      <td>0.115797</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>27.9</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>98.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.200000</td>\n",
-       "      <td>0.387097</td>\n",
-       "      <td>0.216802</td>\n",
-       "      <td>0.267813</td>\n",
-       "      <td>0.090336</td>\n",
-       "      <td>0.258333</td>\n",
-       "      <td>0.150510</td>\n",
-       "      <td>0.160804</td>\n",
-       "      <td>0.268412</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.410445</td>\n",
-       "      <td>0.136714</td>\n",
-       "      <td>0.214586</td>\n",
-       "      <td>69.8</td>\n",
-       "      <td>27.3</td>\n",
-       "      <td>35.4</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>81.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.204819</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.161290</td>\n",
-       "      <td>0.108401</td>\n",
-       "      <td>0.169687</td>\n",
-       "      <td>0.049370</td>\n",
-       "      <td>0.016667</td>\n",
-       "      <td>0.295918</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.097966</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.383366</td>\n",
-       "      <td>0.244395</td>\n",
-       "      <td>0.104690</td>\n",
-       "      <td>76.2</td>\n",
-       "      <td>25.8</td>\n",
-       "      <td>36.8</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>104.0</td>\n",
-       "      <td>65.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.337349</td>\n",
-       "      <td>0.053571</td>\n",
-       "      <td>0.161290</td>\n",
-       "      <td>0.178862</td>\n",
-       "      <td>0.981250</td>\n",
-       "      <td>0.076681</td>\n",
-       "      <td>0.096795</td>\n",
-       "      <td>0.132653</td>\n",
-       "      <td>0.130653</td>\n",
-       "      <td>0.735796</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.258027</td>\n",
-       "      <td>0.030136</td>\n",
-       "      <td>0.106816</td>\n",
-       "      <td>80.3</td>\n",
-       "      <td>29.5</td>\n",
-       "      <td>41.2</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>125.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.216867</td>\n",
-       "      <td>0.082143</td>\n",
-       "      <td>0.274194</td>\n",
-       "      <td>0.192412</td>\n",
-       "      <td>0.063437</td>\n",
-       "      <td>0.067227</td>\n",
-       "      <td>0.104487</td>\n",
-       "      <td>0.262755</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.051438</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.102515</td>\n",
-       "      <td>0.191106</td>\n",
-       "      <td>0.100655</td>\n",
-       "      <td>70.3</td>\n",
-       "      <td>24.9</td>\n",
-       "      <td>31.1</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>121.0</td>\n",
-       "      <td>69.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>0.590361</td>\n",
-       "      <td>0.117857</td>\n",
-       "      <td>0.290323</td>\n",
-       "      <td>0.306233</td>\n",
-       "      <td>0.371563</td>\n",
-       "      <td>0.239496</td>\n",
-       "      <td>0.126923</td>\n",
-       "      <td>0.357143</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.337152</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.778723</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.501020</td>\n",
-       "      <td>94.0</td>\n",
-       "      <td>32.1</td>\n",
-       "      <td>30.7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>94.0</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>81.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>139</th>\n",
-       "      <td>0.614458</td>\n",
-       "      <td>0.039286</td>\n",
-       "      <td>0.161290</td>\n",
-       "      <td>0.230352</td>\n",
-       "      <td>0.132812</td>\n",
-       "      <td>0.067227</td>\n",
-       "      <td>0.082051</td>\n",
-       "      <td>0.119898</td>\n",
-       "      <td>0.193467</td>\n",
-       "      <td>0.098200</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.630948</td>\n",
-       "      <td>0.177876</td>\n",
-       "      <td>0.353855</td>\n",
-       "      <td>91.1</td>\n",
-       "      <td>28.8</td>\n",
-       "      <td>28.3</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>139.0</td>\n",
-       "      <td>61.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>0.289157</td>\n",
-       "      <td>0.060714</td>\n",
-       "      <td>0.354839</td>\n",
-       "      <td>0.205962</td>\n",
-       "      <td>0.099375</td>\n",
-       "      <td>0.504202</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>0.673469</td>\n",
-       "      <td>0.241206</td>\n",
-       "      <td>0.305120</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.392263</td>\n",
-       "      <td>0.118706</td>\n",
-       "      <td>0.305827</td>\n",
-       "      <td>102.9</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>20.3</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>118.0</td>\n",
-       "      <td>71.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>141</th>\n",
-       "      <td>0.301205</td>\n",
-       "      <td>0.010714</td>\n",
-       "      <td>0.338710</td>\n",
-       "      <td>0.168022</td>\n",
-       "      <td>0.495000</td>\n",
-       "      <td>0.274160</td>\n",
-       "      <td>0.091667</td>\n",
-       "      <td>0.076531</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.398644</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.854932</td>\n",
-       "      <td>0.277104</td>\n",
-       "      <td>0.431776</td>\n",
-       "      <td>92.7</td>\n",
-       "      <td>29.3</td>\n",
-       "      <td>25.1</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>71.0</td>\n",
-       "      <td>114.0</td>\n",
-       "      <td>61.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>0.421687</td>\n",
-       "      <td>0.075000</td>\n",
-       "      <td>0.177419</td>\n",
-       "      <td>0.216802</td>\n",
-       "      <td>0.199063</td>\n",
-       "      <td>0.101891</td>\n",
-       "      <td>0.210897</td>\n",
-       "      <td>0.130102</td>\n",
-       "      <td>0.165829</td>\n",
-       "      <td>0.200842</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.517215</td>\n",
-       "      <td>0.149945</td>\n",
-       "      <td>0.245347</td>\n",
-       "      <td>90.3</td>\n",
-       "      <td>26.7</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>47.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>122 rows × 26 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              CA      CA.G      CA.S  Total.CA     DHPAA   DHPAA.G  DHPAA.GG  \\\n",
-       "numVol                                                                         \n",
-       "1       0.602410  0.046429  0.629032  0.311653  0.105938  0.105042  0.063462   \n",
-       "2       0.000000  0.200000  0.387097  0.216802  0.267813  0.090336  0.258333   \n",
-       "3       0.204819  0.000000  0.161290  0.108401  0.169687  0.049370  0.016667   \n",
-       "4       0.337349  0.053571  0.161290  0.178862  0.981250  0.076681  0.096795   \n",
-       "5       0.216867  0.082143  0.274194  0.192412  0.063437  0.067227  0.104487   \n",
-       "...          ...       ...       ...       ...       ...       ...       ...   \n",
-       "138     0.590361  0.117857  0.290323  0.306233  0.371563  0.239496  0.126923   \n",
-       "139     0.614458  0.039286  0.161290  0.230352  0.132812  0.067227  0.082051   \n",
-       "140     0.289157  0.060714  0.354839  0.205962  0.099375  0.504202  0.300000   \n",
-       "141     0.301205  0.010714  0.338710  0.168022  0.495000  0.274160  0.091667   \n",
-       "142     0.421687  0.075000  0.177419  0.216802  0.199063  0.101891  0.210897   \n",
-       "\n",
-       "        DHPAA.GS  DHPAA.SS  Total.DHPAA  ...     VA.GS     VA.SS  Total.VA  \\\n",
-       "numVol                                   ...                                 \n",
-       "1       0.165816  0.000000     0.067805  ...  0.088201  0.256156  0.115797   \n",
-       "2       0.150510  0.160804     0.268412  ...  0.410445  0.136714  0.214586   \n",
-       "3       0.295918  0.000000     0.097966  ...  0.383366  0.244395  0.104690   \n",
-       "4       0.132653  0.130653     0.735796  ...  0.258027  0.030136  0.106816   \n",
-       "5       0.262755  0.000000     0.051438  ...  0.102515  0.191106  0.100655   \n",
-       "...          ...       ...          ...  ...       ...       ...       ...   \n",
-       "138     0.357143  0.000000     0.337152  ...  0.778723  1.000000  0.501020   \n",
-       "139     0.119898  0.193467     0.098200  ...  0.630948  0.177876  0.353855   \n",
-       "140     0.673469  0.241206     0.305120  ...  0.392263  0.118706  0.305827   \n",
-       "141     0.076531  0.000000     0.398644  ...  0.854932  0.277104  0.431776   \n",
-       "142     0.130102  0.165829     0.200842  ...  0.517215  0.149945  0.245347   \n",
-       "\n",
-       "        Weight   BMI   Fat  CVRI  Bpmin  Bpmax  Frec  \n",
-       "numVol                                                \n",
-       "1         75.0  27.9  41.0   7.0   85.0  120.0  98.0  \n",
-       "2         69.8  27.3  35.4   6.0   77.0   99.0  81.0  \n",
-       "3         76.2  25.8  36.8   7.0   78.0  104.0  65.0  \n",
-       "4         80.3  29.5  41.2   8.0   80.0  125.0   0.0  \n",
-       "5         70.3  24.9  31.1   5.0   62.0  121.0  69.0  \n",
-       "...        ...   ...   ...   ...    ...    ...   ...  \n",
-       "138       94.0  32.1  30.7   7.0   94.0  131.0  81.0  \n",
-       "139       91.1  28.8  28.3   8.0   99.0  139.0  61.0  \n",
-       "140      102.9  27.0  20.3   6.0   82.0  118.0  71.0  \n",
-       "141       92.7  29.3  25.1  13.0   71.0  114.0  61.0  \n",
-       "142       90.3  26.7  19.4   9.0   78.0  131.0  47.0  \n",
-       "\n",
-       "[122 rows x 26 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
+    "df, df_name = fullRead(paths[3], sep = \",\", full = False)\n",
+    "#Sweetener: object, Sex: object, Weight: object, BMI: object, Fat: object, CVRI: object, Bpmin: object, Bpmax: object, Frec: object\n",
+    "full = True\n",
+    "if not full:\n",
+    "    df[['Weight', 'BMI', \"Fat\", \"CVRI\", \"Bpmin\", \"Bpmax\", \"Frec\"]] = df[['Weight', 'BMI', \"Fat\", \"CVRI\", \"Bpmin\", \"Bpmax\", \"Frec\"]].apply(pd.to_numeric)\n",
+    "df.fillna(0, inplace=True)\n",
+    "df[['Sex', 'Sweetener']] = df[['Sex', 'Sweetener']].astype(\"category\")\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b3926df6-d686-4cd5-b7ca-39b2b4a0705d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
+   "id": "9262fea9-96be-4337-83ed-669ef354f51a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Observaciones en train: (80, 23)\n",
+      "Observaciones en validation: (20, 23)\n",
+      "Observaciones en test: (25, 23)\n"
+     ]
+    }
+   ],
    "source": [
-    "y_test = pd.read_csv(os.path.join(\"data\",\"y_metTest_RF_Met_lightTrainplasmAnt.csv\"), index_col = 0)\n",
-    "X_test = pd.read_csv(os.path.join(\"data\",\"X_metTest_RF_Met_lightTrainplasmAnt.csv\"), index_col = 0)"
+    "X = df[df[\"Time\"] == \"Initial\"].set_index(\"numVol\").drop([\"Time\"], axis=1) \n",
+    "y = df[df[\"Time\"] == \"Final\"].set_index(\"numVol\").drop([\"Time\", \"Sweetener\", \"Sex\"], axis = 1)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X,\n",
+    "    y,\n",
+    "    train_size = 0.8,\n",
+    "    random_state = 42,\n",
+    "    shuffle = True\n",
+    ")\n",
+    "\n",
+    "X_train, X_val, y_train, y_val = train_test_split(\n",
+    "    X_train,\n",
+    "    y_train,\n",
+    "    train_size   = 0.8,\n",
+    "    random_state = 42,\n",
+    "    shuffle      = True\n",
+    ")\n",
+    "\n",
+    "print(\"Observaciones en train:\", X_train.shape)\n",
+    "print(\"Observaciones en validation:\", X_val.shape)\n",
+    "print(\"Observaciones en test:\", X_test.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "d8f5dd02-a95c-468d-a027-13ead18460d7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import pickle\n",
-    "\n",
-    "modelPath = os.path.join(\"data\",\"RF_Met_lightTrainplasmAnt.pkl\")\n",
-    "\n",
-    "modelname = re.sub(\".pkl\",\"\",re.sub(\"model+(RF|XGB|MLP)+(Met|Full)\",\"\", modelPath))\n",
-    "\n",
-    "# define model type\n",
-    "if re.match(\"Full\", modelPath):\n",
-    "    met = True\n",
-    "\n",
-    "# open the model\n",
-    "\n",
-    "with open (modelPath, 'rb') as f:\n",
-    "    model = pickle.load(f)\n",
-    "\n",
-    "df_pred = model.predict(X_test)\n",
-    "y_pred = pd.DataFrame([i for i in df_pred], index = y_test.index, columns= y_test.columns)\n",
-    "y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "b3e4308d-5ace-4fa0-8f81-cdda1d632654",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 15,
+   "id": "6891333f-12f2-4c01-84cd-aa862e819b60",
+   "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CA</th>\n",
+       "      <th>CA.G</th>\n",
+       "      <th>CA.S</th>\n",
+       "      <th>CA.GS</th>\n",
+       "      <th>Total.CA</th>\n",
+       "      <th>DHPAA</th>\n",
+       "      <th>DHPAA.G</th>\n",
+       "      <th>DHPAA.GG</th>\n",
+       "      <th>DHPAA.GS</th>\n",
+       "      <th>DHPAA.SS</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TFA.G</th>\n",
+       "      <th>TFA.S</th>\n",
+       "      <th>TFA.di.sulfate.1</th>\n",
+       "      <th>Total.TFA</th>\n",
+       "      <th>TIFA.Sulfate.1</th>\n",
+       "      <th>VA</th>\n",
+       "      <th>VA.GG</th>\n",
+       "      <th>VA.GS</th>\n",
+       "      <th>VA.SS</th>\n",
+       "      <th>Total.VA</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>numVol</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
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+       "      <td>0.169173</td>\n",
+       "      <td>0.028835</td>\n",
+       "      <td>0.009804</td>\n",
+       "      <td>0.220762</td>\n",
+       "      <td>0.036111</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.008556</td>\n",
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+       "      <td>0.000000</td>\n",
+       "      <td>0.202429</td>\n",
+       "      <td>0.005972</td>\n",
+       "      <td>0.035749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>0.061947</td>\n",
+       "      <td>0.180421</td>\n",
+       "      <td>0.946388</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>2.347</td>\n",
+       "      <td>0.032425</td>\n",
+       "      <td>0.044644</td>\n",
+       "      <td>0.003268</td>\n",
+       "      <td>0.454665</td>\n",
+       "      <td>0.109127</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.046886</td>\n",
+       "      <td>0.153368</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.155492</td>\n",
+       "      <td>0.047606</td>\n",
+       "      <td>0.057156</td>\n",
+       "      <td>0.152228</td>\n",
+       "      <td>0.257085</td>\n",
+       "      <td>0.129526</td>\n",
+       "      <td>0.077941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.035398</td>\n",
+       "      <td>0.014513</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>0.260808</td>\n",
+       "      <td>0.062949</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.006570</td>\n",
+       "      <td>0.061905</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.002053</td>\n",
+       "      <td>0.288729</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.289227</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.026012</td>\n",
+       "      <td>0.283416</td>\n",
+       "      <td>0.149460</td>\n",
+       "      <td>0.023143</td>\n",
+       "      <td>0.038730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>0.026549</td>\n",
+       "      <td>0.006545</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.025923</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.152431</td>\n",
+       "      <td>0.048016</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003422</td>\n",
+       "      <td>0.087068</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>0.087775</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.029361</td>\n",
+       "      <td>0.180693</td>\n",
+       "      <td>0.114035</td>\n",
+       "      <td>0.017171</td>\n",
+       "      <td>0.038664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0.023009</td>\n",
+       "      <td>0.143142</td>\n",
+       "      <td>0.086907</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.679</td>\n",
+       "      <td>0.150846</td>\n",
+       "      <td>0.084061</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.074901</td>\n",
+       "      <td>0.067460</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.095825</td>\n",
+       "      <td>0.027406</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.031116</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.012581</td>\n",
+       "      <td>0.065594</td>\n",
+       "      <td>0.068489</td>\n",
+       "      <td>0.027996</td>\n",
+       "      <td>0.018669</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "(Index([ 37,  91,  95, 179,   9,  80, 125, 215,  63, 111, 107, 239,  21, 181,\n",
-       "        219,  23, 153, 112, 231,   0,  53,  89, 133, 197,  49,  84, 211, 187,\n",
-       "         73, 200,  25,  31, 145,  45, 233, 183, 166],\n",
-       "       dtype='int64'),\n",
-       " Index([ 36,  90,  94, 178,   8,  81, 124, 214,  62, 110, 106, 238,  20, 180,\n",
-       "        218,  22, 152, 113, 230,   1,  52,  88, 132, 196,  48,  85, 210, 186,\n",
-       "         72, 201,  24,  30, 144,  44, 232, 182, 167],\n",
-       "       dtype='int64'))"
+       "              CA      CA.G      CA.S  CA.GS  Total.CA     DHPAA   DHPAA.G  \\\n",
+       "numVol                                                                      \n",
+       "21      0.010619  0.025612  0.258465  0.002     0.561  0.169173  0.028835   \n",
+       "52      0.061947  0.180421  0.946388  0.005     2.347  0.032425  0.044644   \n",
+       "40      0.035398  0.014513  0.000000  0.007     0.085  0.260808  0.062949   \n",
+       "88      0.026549  0.006545  0.000000  0.004     0.046  0.000000  0.025923   \n",
+       "64      0.023009  0.143142  0.086907  0.004     0.679  0.150846  0.084061   \n",
+       "\n",
+       "        DHPAA.GG  DHPAA.GS  DHPAA.SS  ...     TFA.G     TFA.S  \\\n",
+       "numVol                                ...                       \n",
+       "21      0.009804  0.220762  0.036111  ...  0.008556  0.120797   \n",
+       "52      0.003268  0.454665  0.109127  ...  0.046886  0.153368   \n",
+       "40      0.000000  0.006570  0.061905  ...  0.002053  0.288729   \n",
+       "88      0.000000  0.152431  0.048016  ...  0.003422  0.087068   \n",
+       "64      0.000000  0.074901  0.067460  ...  0.095825  0.027406   \n",
+       "\n",
+       "        TFA.di.sulfate.1  Total.TFA  TIFA.Sulfate.1        VA     VA.GG  \\\n",
+       "numVol                                                                    \n",
+       "21                  0.00   0.121585        0.000000  0.025012  0.000000   \n",
+       "52                  0.52   0.155492        0.047606  0.057156  0.152228   \n",
+       "40                  0.12   0.289227        0.000000  0.026012  0.283416   \n",
+       "88                  0.16   0.087775        0.000000  0.029361  0.180693   \n",
+       "64                  0.28   0.031116        0.000000  0.012581  0.065594   \n",
+       "\n",
+       "           VA.GS     VA.SS  Total.VA  \n",
+       "numVol                                \n",
+       "21      0.202429  0.005972  0.035749  \n",
+       "52      0.257085  0.129526  0.077941  \n",
+       "40      0.149460  0.023143  0.038730  \n",
+       "88      0.114035  0.017171  0.038664  \n",
+       "64      0.068489  0.027996  0.018669  \n",
+       "\n",
+       "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "X_test.index,y_test.index"
+    "y_test.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "7b01c0d4-d972-4788-9daf-394cf89a70a8",
+   "execution_count": 16,
+   "id": "04043c65-fbb4-430c-8fbb-c02695cd5ed7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CA</th>\n",
+       "      <th>CA.G</th>\n",
+       "      <th>CA.S</th>\n",
+       "      <th>CA.GS</th>\n",
+       "      <th>Total.CA</th>\n",
+       "      <th>DHPAA</th>\n",
+       "      <th>DHPAA.G</th>\n",
+       "      <th>DHPAA.GG</th>\n",
+       "      <th>DHPAA.GS</th>\n",
+       "      <th>DHPAA.SS</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TFA.di.sulfate.1</th>\n",
+       "      <th>Total.TFA</th>\n",
+       "      <th>TIFA.Sulfate.1</th>\n",
+       "      <th>VA</th>\n",
+       "      <th>VA.GG</th>\n",
+       "      <th>VA.GS</th>\n",
+       "      <th>VA.SS</th>\n",
+       "      <th>Total.VA</th>\n",
+       "      <th>Sweetener</th>\n",
+       "      <th>Sex</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>numVol</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "  <tbody>\n",
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+       "      <td>0.070</td>\n",
+       "      <td>0.083647</td>\n",
+       "      <td>0.064405</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.285151</td>\n",
+       "      <td>0.034524</td>\n",
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+       "      <td>0.310391</td>\n",
+       "      <td>0.064203</td>\n",
+       "      <td>0.035765</td>\n",
+       "      <td>ST</td>\n",
+       "      <td>MAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>0.030088</td>\n",
+       "      <td>0.112123</td>\n",
+       "      <td>0.422122</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>1.153</td>\n",
+       "      <td>0.045583</td>\n",
+       "      <td>0.025065</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.145861</td>\n",
+       "      <td>0.198413</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.038008</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.002416</td>\n",
+       "      <td>0.861386</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.167600</td>\n",
+       "      <td>0.071182</td>\n",
+       "      <td>SU</td>\n",
+       "      <td>WOMAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0.035398</td>\n",
+       "      <td>0.014798</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>0.153665</td>\n",
+       "      <td>0.014327</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.381078</td>\n",
+       "      <td>0.056349</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.059072</td>\n",
+       "      <td>0.016282</td>\n",
+       "      <td>0.003683</td>\n",
+       "      <td>0.167079</td>\n",
+       "      <td>0.444669</td>\n",
+       "      <td>0.087719</td>\n",
+       "      <td>0.032336</td>\n",
+       "      <td>ST</td>\n",
+       "      <td>MAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>0.019469</td>\n",
+       "      <td>0.028742</td>\n",
+       "      <td>0.071670</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.248</td>\n",
+       "      <td>0.039944</td>\n",
+       "      <td>0.004290</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.181340</td>\n",
+       "      <td>0.071429</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.059678</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.010865</td>\n",
+       "      <td>0.064356</td>\n",
+       "      <td>0.177126</td>\n",
+       "      <td>0.057111</td>\n",
+       "      <td>0.023589</td>\n",
+       "      <td>SU</td>\n",
+       "      <td>MAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0.014159</td>\n",
+       "      <td>0.009106</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>0.071429</td>\n",
+       "      <td>0.141264</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.114323</td>\n",
+       "      <td>0.088492</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.067890</td>\n",
+       "      <td>0.027472</td>\n",
+       "      <td>0.027545</td>\n",
+       "      <td>0.227723</td>\n",
+       "      <td>0.244939</td>\n",
+       "      <td>0.282941</td>\n",
+       "      <td>0.055727</td>\n",
+       "      <td>SU</td>\n",
+       "      <td>WOMAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              CA      CA.G      CA.S  CA.GS  Total.CA     DHPAA   DHPAA.G  \\\n",
+       "numVol                                                                      \n",
+       "21      0.023009  0.013375  0.000000  0.004     0.070  0.083647  0.064405   \n",
+       "52      0.030088  0.112123  0.422122  0.009     1.153  0.045583  0.025065   \n",
+       "40      0.035398  0.014798  0.000000  0.004     0.078  0.153665  0.014327   \n",
+       "88      0.019469  0.028742  0.071670  0.002     0.248  0.039944  0.004290   \n",
+       "64      0.014159  0.009106  0.000000  0.003     0.047  0.071429  0.141264   \n",
+       "\n",
+       "        DHPAA.GG  DHPAA.GS  DHPAA.SS  ...  TFA.di.sulfate.1  Total.TFA  \\\n",
+       "numVol                                ...                                \n",
+       "21           0.0  0.285151  0.034524  ...              0.00   0.050904   \n",
+       "52           0.0  0.145861  0.198413  ...              0.00   0.038008   \n",
+       "40           0.0  0.381078  0.056349  ...              0.00   0.059072   \n",
+       "88           0.0  0.181340  0.071429  ...              0.00   0.059678   \n",
+       "64           0.0  0.114323  0.088492  ...              0.24   0.067890   \n",
+       "\n",
+       "        TIFA.Sulfate.1        VA     VA.GG     VA.GS     VA.SS  Total.VA  \\\n",
+       "numVol                                                                     \n",
+       "21            0.000000  0.016264  0.060644  0.310391  0.064203  0.035765   \n",
+       "52            0.000000  0.002416  0.861386  1.000000  0.167600  0.071182   \n",
+       "40            0.016282  0.003683  0.167079  0.444669  0.087719  0.032336   \n",
+       "88            0.000000  0.010865  0.064356  0.177126  0.057111  0.023589   \n",
+       "64            0.027472  0.027545  0.227723  0.244939  0.282941  0.055727   \n",
+       "\n",
+       "        Sweetener    Sex  \n",
+       "numVol                    \n",
+       "21             ST    MAN  \n",
+       "52             SU  WOMAN  \n",
+       "40             ST    MAN  \n",
+       "88             SU    MAN  \n",
+       "64             SU  WOMAN  \n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "dummies_sweetener = pd.from_dummies(X_test[['Sweetener_SA', 'Sweetener_ST','Sweetener_SU']]).replace(\"Sweetener_\", \"\",regex=True).set_index(X_test.index-1)\n",
-    "dummies_sex = pd.from_dummies(X_test[['Sex_MAN', \"Sex_WOMAN\"]]).replace(\"Sex_\", \"\",regex=True).set_index(X_test.index-1)\n",
+    "X_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "397cc7f2-0b2a-4ebb-ae27-3297e80845ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Best trial: 28. Best value: 0.0867241:   8%| | 40/500 [10:39<2:02:37"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mejores hiperparámetros: {'n_estimators': 5800, 'max_depth': 155, 'reg_lambda': 0.0004044061785086238, 'reg_alpha': 0.0002213604600570743, 'gamma': 57382.19782645314, 'subsample': 0.22318644387511533, 'colsample_bynode': 0.14085090074121356}\n",
+      "Mejor score: 0.08672414606723956\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Búsqueda bayesiana de hiperparámetros con optuna\n",
+    "# ==============================================================================\n",
+    "def objective(trial):\n",
+    "    params = {\n",
+    "        'n_estimators': trial.suggest_int('n_estimators', 10, 10000, step=10),\n",
+    "        'max_depth': trial.suggest_int('max_depth', 3, 300),\n",
+    "        'reg_lambda': trial.suggest_float('reg_lambda', 1e-5, 1e+10, log=True),\n",
+    "        'reg_alpha': trial.suggest_float('reg_alpha', 1e-5, 1e+10, log=True),\n",
+    "        'gamma': trial.suggest_float('gamma', 1e-5, 1e+10, log=True),\n",
+    "        'subsample': trial.suggest_float('subsample', 0.1, 1),\n",
+    "        'colsample_bynode': trial.suggest_float('colsample_bynode', 0.1, 1),\n",
+    "    }\n",
+    "\n",
+    "    model = XGBRFRegressor(\n",
+    "                    tree_method        = 'hist',\n",
+    "                    grow_policy        = 'depthwise',\n",
+    "                    learning_rate      = 1.0,\n",
+    "                    n_jobs             = -1,\n",
+    "                    random_state       = 42,\n",
+    "                    enable_categorical = True,\n",
+    "                    **params\n",
+    "            )\n",
+    "    model.fit(X_train, y_train)\n",
+    "    predictions = model.predict(X_val)\n",
+    "    score = mean_squared_error(y_val, predictions, squared=False)\n",
+    "    return score\n",
+    "\n",
+    "study = optuna.create_study()#direction='minimize')\n",
+    "study.optimize(objective, n_trials=500, show_progress_bar=True, timeout=60*10)\n",
+    "\n",
+    "print('Mejores hiperparámetros:', study.best_params)\n",
+    "print('Mejor score:', study.best_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a5a2fb58-9fd8-498f-942a-f81625695578",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tiempo entrenamiento: 9.63 segundos\n",
+      "Tiempo predicción:    0.02 segundos\n",
+      "RMSE:                 0.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Random Forest XGBoost con los mejores hiperparámetros encontrados\n",
+    "# ==============================================================================\n",
+    "model = XGBRFRegressor(\n",
+    "                tree_method        = 'hist',\n",
+    "                grow_policy        = 'depthwise',\n",
+    "                learning_rate      = 1.0,\n",
+    "                n_jobs             = -1,\n",
+    "                random_state       = 42,\n",
+    "                enable_categorical = True,\n",
+    "                missing = 0,\n",
+    "                **study.best_params\n",
+    "             )\n",
+    "\n",
+    "# Entrenamiento del modelo\n",
+    "start = time.time()\n",
+    "model.fit(\n",
+    "        X = pd.concat([X_train, X_val]),\n",
+    "        y = pd.concat([y_train, y_val])\n",
+    ")\n",
+    "end = time.time()\n",
+    "tiempo_entrenamiento_xgboost = end - start\n",
+    "\n",
+    "# Predicciones test\n",
+    "start = time.time()\n",
+    "predicciones = model.predict(X=X_test)\n",
+    "end = time.time()\n",
+    "tiempo_prediccion_xgboost = end - start\n",
+    "\n",
+    "# Error de test del modelo\n",
+    "rmse_rf_xgboost = mean_squared_error(\n",
+    "                      y_true  = y_test,\n",
+    "                      y_pred  = predicciones,\n",
+    "                      squared = False\n",
+    "                  )\n",
+    "                 \n",
+    "print(f\"Tiempo entrenamiento: {tiempo_entrenamiento_xgboost:.2f} segundos\")\n",
+    "print(f\"Tiempo predicción:    {tiempo_prediccion_xgboost:.2f} segundos\")\n",
+    "print(f\"RMSE:                 {rmse_rf_xgboost:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a1161615-4a5f-45f2-ab4b-5687446464b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "        0.16875592, 0.08649335, 0.03183271, 0.16071796, 0.04629645,\n",
+       "        0.10174754, 0.01840909, 0.15015055, 0.1148233 , 0.15078218,\n",
+       "        0.01638437, 0.07284593, 0.15038043, 0.14962094, 0.04967729,\n",
+       "        0.08432022],\n",
+       "       [0.04251856, 0.08250073, 0.10186668, 0.0044426 , 0.50393957,\n",
+       "        0.16875592, 0.08649335, 0.03183271, 0.16071796, 0.04629645,\n",
+       "        0.10174754, 0.01840909, 0.15015055, 0.1148233 , 0.15078218,\n",
+       "        0.01638437, 0.07284593, 0.15038043, 0.14962094, 0.04967729,\n",
+       "        0.08432022]], dtype=float32)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9d97affd-fdbc-426b-be71-f4ef0665610e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ----------------- CA MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite CA MAE: 0.038 (0.030)\n",
+      " ----------------- CA.G MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite CA.G MAE: 0.055 (0.017)\n",
+      " ----------------- CA.S MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite CA.S MAE: 0.189 (0.096)\n",
+      " ----------------- CA.GS MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite CA.GS MAE: 0.003 (0.001)\n",
+      " ----------------- Total.CA MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite Total.CA MAE: 0.404 (0.213)\n",
+      " ----------------- DHPAA MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite DHPAA MAE: 0.117 (0.059)\n",
+      " ----------------- DHPAA.G MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite DHPAA.G MAE: 0.055 (0.028)\n",
+      " ----------------- DHPAA.GG MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite DHPAA.GG MAE: 0.024 (0.014)\n",
+      " ----------------- DHPAA.GS MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite DHPAA.GS MAE: 0.101 (0.046)\n",
+      " ----------------- DHPAA.SS MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite DHPAA.SS MAE: 0.027 (0.012)\n",
+      " ----------------- Total.DHPAA MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite Total.DHPAA MAE: 0.055 (0.029)\n",
+      " ----------------- TFA.G MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite TFA.G MAE: 0.017 (0.008)\n",
+      " ----------------- TFA.S MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite TFA.S MAE: 0.094 (0.038)\n",
+      " ----------------- TFA.di.sulfate.1 MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite TFA.di.sulfate.1 MAE: 0.116 (0.050)\n",
+      " ----------------- Total.TFA MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite Total.TFA MAE: 0.094 (0.038)\n",
+      " ----------------- TIFA.Sulfate.1 MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite TIFA.Sulfate.1 MAE: 0.017 (0.007)\n",
+      " ----------------- VA MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite VA MAE: 0.047 (0.024)\n",
+      " ----------------- VA.GG MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite VA.GG MAE: 0.096 (0.040)\n",
+      " ----------------- VA.GS MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite VA.GS MAE: 0.097 (0.046)\n",
+      " ----------------- VA.SS MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite VA.SS MAE: 0.026 (0.011)\n",
+      " ----------------- Total.VA MODEL EVALUATION ----------------- \n",
+      "Full model pruebaRF for metabolite Total.VA MAE: 0.050 (0.023)\n",
+      " ----------------- MEAN PERFORMANCE EVALUATION ----------------- \n",
+      "Mean performance of all modelpruebaRF MAE: 0.082 (0.019)\n"
+     ]
+    }
+   ],
+   "source": [
+    "scores = []\n",
+    "mean_scores = []\n",
+    "metabs = pd.Series(y_test.columns)\n",
+    "modelname = \"pruebaRF\"\n",
+    "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=42)\n",
+    "\n",
+    "for n in metabs:\n",
+    "    \n",
+    "    print(\" ----------------- \" + n + \" MODEL EVALUATION ----------------- \")\n",
+    "\n",
+    "    n_scores = cross_val_score(model, X_test, y_test[n], scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n",
+    "    n_scores = np.absolute(n_scores)\n",
+    "    scores.append(n_scores) \n",
+    "    mean_scores.append(np.mean(n_scores))\n",
+    "\n",
+    "    print('Full model ' + modelname + ' for metabolite '+n+ ' MAE: %.3f (%.3f)' % (np.mean(n_scores), np.std(n_scores)))\n",
+    "\n",
+    "# full model evaluation\n",
+    "print(\" ----------------- MEAN PERFORMANCE EVALUATION ----------------- \")\n",
+    "\n",
+    "n_score_all = cross_val_score(model, X_test, y_test, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n",
+    "n_score_all = np.absolute(n_score_all)\n",
+    "scores.append((n_score_all))\n",
+    "mean_scores.append(np.mean(n_score_all))\n",
+    "df_scores = pd.concat([metabs, pd.Series(scores), pd.Series(mean_scores)], axis = 1, ignore_index = True)\n",
+    "df_scores.columns = [\"Metabolite modeled\", \"MAE score\", \"Mean MAE score\"]\n",
+    "df_scores.to_csv(\"../results/predicts/scores_\" + modelname +\".csv\", sep = \",\")\n",
+    "\n",
+    "print(\"Mean performance of all model\" + modelname +  \" MAE: %.3f (%.3f)\" % (np.mean(n_score_all), np.std(n_score_all)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "949169e3-2419-499b-b068-f798d06a8f7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ----------------- PLOTTING STARTING ----------------- \n",
+      " ----------------- PREDICTING AND JOINING PRED + TEST ----------------- \n",
+      " ----------------- STARTING PLOTTING ----------------- \n",
+      " ----------------- PREDICTED VS TEST CA ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS CA ----------------- \n",
+      " ----------------- PREDICTED VS TEST CA.G ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS CA.G ----------------- \n",
+      " ----------------- PREDICTED VS TEST CA.S ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS CA.S ----------------- \n",
+      " ----------------- PREDICTED VS TEST CA.GS ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS CA.GS ----------------- \n",
+      " ----------------- PREDICTED VS TEST Total.CA ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS Total.CA ----------------- \n",
+      " ----------------- PREDICTED VS TEST DHPAA ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS DHPAA ----------------- \n",
+      " ----------------- PREDICTED VS TEST DHPAA.G ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS DHPAA.G ----------------- \n",
+      " ----------------- PREDICTED VS TEST DHPAA.GG ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS DHPAA.GG ----------------- \n",
+      " ----------------- PREDICTED VS TEST DHPAA.GS ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS DHPAA.GS ----------------- \n",
+      " ----------------- PREDICTED VS TEST DHPAA.SS ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS DHPAA.SS ----------------- \n",
+      " ----------------- PREDICTED VS TEST Total.DHPAA ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS Total.DHPAA ----------------- \n",
+      " ----------------- PREDICTED VS TEST TFA.G ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS TFA.G ----------------- \n",
+      " ----------------- PREDICTED VS TEST TFA.S ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS TFA.S ----------------- \n",
+      " ----------------- PREDICTED VS TEST TFA.di.sulfate.1 ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS TFA.di.sulfate.1 ----------------- \n",
+      " ----------------- PREDICTED VS TEST Total.TFA ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS Total.TFA ----------------- \n",
+      " ----------------- PREDICTED VS TEST TIFA.Sulfate.1 ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS TIFA.Sulfate.1 ----------------- \n",
+      " ----------------- PREDICTED VS TEST VA ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS VA ----------------- \n",
+      " ----------------- PREDICTED VS TEST VA.GG ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS VA.GG ----------------- \n",
+      " ----------------- PREDICTED VS TEST VA.GS ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS VA.GS ----------------- \n",
+      " ----------------- PREDICTED VS TEST VA.SS ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS VA.SS ----------------- \n",
+      " ----------------- PREDICTED VS TEST Total.VA ----------------- \n",
+      " ----------------- PREDICTED VS RESIDUALS Total.VA ----------------- \n"
+     ]
+    }
+   ],
+   "source": [
+    "# plotting\n",
+    "\n",
+    "print(\" ----------------- PLOTTING STARTING ----------------- \")\n",
+    "\n",
+    "# inherit model, modelname, df, df_name, testSplits\n",
+    "print(\" ----------------- PREDICTING AND JOINING PRED + TEST ----------------- \")\n",
     "\n",
     "# make predict\n",
     "y_pred = model.predict(X_test)\n",
     "# pred_prueba_DF = pd.DataFrame(pred_prueba, index = X_test.index-1, columns=y_test.columns).add_suffix(\"_pred\").join(y_test.add_suffix('_test')).fillna(0) \n",
     "df_predTest = pd.DataFrame([i for i in y_pred], index = y_test.index, columns= y_test.columns).add_suffix(\"_pred\").join(y_test.add_suffix('_test')).fillna(0)\n",
-    "df_predTest[\"Sex\"] = dummies_sex\n",
-    "df_predTest[\"Sweetener\"] = dummies_sweetener\n",
-    "df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv(\"DF_predTest_Met\"+modelname+\".csv\", sep = \",\")\n",
-    "\n",
+    "df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv(\"../results/predicts/df_pred-test_\"+modelname+\".csv\", sep = \",\")\n",
+    "    \n",
     "# print plots\n",
-    "metabs = y_test.columns.drop(list(y_test.filter(regex='Sex|Sweetener'))) "
+    "metabs = y_test.columns.drop(list(y_test.filter(regex='Sex|Sweetener'))) \n",
+    "\n",
+    "print(\" ----------------- STARTING PLOTTING ----------------- \")\n",
+    "\n",
+    "\n",
+    "for metab in metabs:\n",
+    "    #sns.regplot(data = pred_prueba_DF, x=metab+\"_pred\", y=metab+\"_test\", )\n",
+    "    \n",
+    "    print(\" ----------------- PREDICTED VS TEST \" + metab + \" ----------------- \")\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    adjustAxis = True\n",
+    "    if (adjustAxis):\n",
+    "        ax = plt.gca()\n",
+    "        minPred = min(df_predTest[metab+\"_pred\"])\n",
+    "        maxPred = max(df_predTest[metab+\"_pred\"])\n",
+    "        minTest = min(df_predTest[metab+\"_test\"])\n",
+    "        maxTest = max(df_predTest[metab+\"_test\"])\n",
+    "        ax.set_xlim([min(minPred,  minTest),  max(maxPred,  maxTest)])\n",
+    "        ax.set_ylim([min(minPred,  minTest),  max(maxPred,  maxTest)])\n",
+    "\n",
+    "    \n",
+    "    plt.figure(figsize = (10,10))\n",
+    "    sns.lmplot(data = df_predTest, x=metab+\"_pred\", y=metab+\"_test\")\n",
+    "    plt.title(\"TEST VS PREDICTED lmplot \" + metab + \" \" + modelname)\n",
+    "    plt.savefig(\"../results/plots/testVsPredicted_lmplot_\"+ metab+\"_\"+modelname+\".png\")\n",
+    "    plt.close(\"all\")   \n",
+    "\n",
+    "    print(\" ----------------- PREDICTED VS RESIDUALS \" + metab + \" ----------------- \")\n",
+    "\n",
+    "    plt.figure(figsize = (10,10))\n",
+    "    y_pred_metab = df_predTest[metab+\"_pred\"]\n",
+    "    residuals = df_predTest[metab+\"_test\"]-df_predTest[metab+\"_pred\"]\n",
+    "    df_all = pd.concat([y_pred_metab, residuals], axis=1)\n",
+    "    df_all.columns = [\"Predicted\", \"Residuals\"]\n",
+    "    \n",
+    "    sns.scatterplot(data = df_all, x=\"Predicted\", y=\"Residuals\")\n",
+    "    plt.title(\"RESIDUALS VS PREDICTED \" + metab + \" \" + modelname)\n",
+    "    plt.axhline(y=0)\n",
+    "    plt.savefig(\"../results/plots/residualsVsPredicted_\"+ metab+\"_\"+modelname+\".png\")\n",
+    "    plt.close(\"all\")   "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4b9551c-5e38-4d2c-8543-aca42b650e40",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1118,7 +1128,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,
diff --git a/scripts/.ipynb_checkpoints/scores_plasmAnt-checkpoint.csv b/scripts/.ipynb_checkpoints/scores_plasmAnt-checkpoint.csv
new file mode 100644
index 0000000..ba5c729
--- /dev/null
+++ b/scripts/.ipynb_checkpoints/scores_plasmAnt-checkpoint.csv
@@ -0,0 +1,27 @@
+,0
+0,CA
+1,CA.G
+2,CA.S
+3,Total.CA
+4,DHPAA
+5,DHPAA.G
+6,DHPAA.GG
+7,DHPAA.GS
+8,DHPAA.SS
+9,Total.DHPAA
+10,TFA.G
+11,TFA.S
+12,Total.TFA
+13,VA
+14,VA.GG
+15,VA.S
+16,VA.GS
+17,VA.SS
+18,Total.VA
+19,Weight
+20,BMI
+21,Fat
+22,CVRI
+23,Bpmin
+24,Bpmax
+25,Frec
diff --git a/scripts/testVsPredicted_lmplot_BMI_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_BMI_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_BMI_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_BMI_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_CA_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_CA_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_CA_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_CA_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_DHPAA.GG_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA.GG_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_DHPAA.GG_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA.GG_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_DHPAA.GS_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA.GS_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_DHPAA.GS_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA.GS_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_DHPAA.G_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA.G_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_DHPAA.G_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA.G_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_DHPAA.SS_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA.SS_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_DHPAA.SS_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA.SS_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_DHPAA_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_DHPAA_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_DHPAA_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_Fat_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_Fat_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_Fat_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_Fat_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_Frec_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_Frec_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_Frec_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_Frec_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_TFA.G_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_TFA.G_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_TFA.G_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_TFA.G_plasmAnt-checkpoint.png
diff --git a/scripts/testVsPredicted_lmplot_VA.GG_plasmAnt.png b/scripts/.ipynb_checkpoints/testVsPredicted_lmplot_VA.GG_plasmAnt-checkpoint.png
similarity index 100%
rename from scripts/testVsPredicted_lmplot_VA.GG_plasmAnt.png
rename to scripts/.ipynb_checkpoints/testVsPredicted_lmplot_VA.GG_plasmAnt-checkpoint.png
diff --git a/scripts/.ipynb_checkpoints/utils-checkpoint.py b/scripts/.ipynb_checkpoints/utils-checkpoint.py
index f26f3a8..cdb19b9 100644
--- a/scripts/.ipynb_checkpoints/utils-checkpoint.py
+++ b/scripts/.ipynb_checkpoints/utils-checkpoint.py
@@ -1,11 +1,13 @@
 import pandas as pd
 import re 
+import os
+import sys
 
 def fullRead(pathToTable, sep, full = False):
 
   df_renamed = pd.read_csv(pathToTable, sep = sep, encoding = "latin_1")
     
-  df_name = re.sub("_ord.csv","",(re.sub("data/", "" ,pathToTable)))
+  df_name = re.sub("_ord.csv","",(re.sub("../data/", "" ,pathToTable)))
   
 
   print ("Reading dataset " + df_name)
@@ -21,7 +23,8 @@ def fullRead(pathToTable, sep, full = False):
     print ("#")
     print ("#")
     print ("#")  
-    df_anthro = pd.read_csv("../data/chronicAnthropometricCardiovascularData.csv", sep=";", decimal=",")
+   
+    df_anthro = pd.read_csv(os.path.join("..","data","chronicAnthropometricCardiovascularData.csv"), sep=";", decimal=",")
     df_renamed = df_renamed.merge(df_anthro)
 
     # separating by time moment and renaming
@@ -57,19 +60,21 @@ def fullRead(pathToTable, sep, full = False):
         
     df_renamed.drop(columns = ["Peso inicial", "Peso final", "Delta Peso", "Talla", "IMC Inicial", "IMC Final", "Delta IMC", "Grasa inicial", "Grasa final", "Delta Grasa", "IRCV Final", "IRCV inicial", "Bpmin final", "Bpmin inicial", "Bpmax final", "Bpmax inicial", "Frec final", "Frec inicial",], inplace=True )
   
-  #   
+    
   df_renamed.drop(columns = ["Unnamed: 0", "grouping"], inplace=True )
   
   print ("Filling NA with zeros")
   print ("#")
   print ("#")
   print ("#")  
-  df_renamed.fillna(0, inplace=True)
   
+  #df_renamed.fillna(0, inplace=True)
+    
   return (df_renamed, df_name)
 
+
 def scaling(df_read):
-   
+    
    import numpy as np
    from sklearn import preprocessing
 
@@ -89,13 +94,13 @@ def encodingSplitting(df, full = True):
     if (full):
         print ("Split for anthropometric + metabolic")
 
-        X, y= df[df["Time_Initial"] == 1].drop(["numVol", "Time_Initial", "Time_Final"], axis=1), df[df["Time_Final"] == 1].drop(['numVol','Sweetener_SA', 'Sweetener_ST','Sweetener_SU','Time_Final', 'Time_Initial', 'Sex_MAN', 'Sex_WOMAN'], axis = 1)
+        X, y= df[df["Time_Initial"] == 1].set_index("numVol").drop(["Time_Initial", "Time_Final"], axis=1), df[df["Time_Final"] == 1].set_index("numVol").drop(['Sweetener_SA', 'Sweetener_ST','Sweetener_SU','Time_Final', 'Time_Initial', 'Sex_MAN', 'Sex_WOMAN'], axis = 1)
         
     else:
         print ("Split for only metabolic")
-        X, y = df[df["Time_Initial"] == 1].drop(["numVol", "Time_Initial", "Time_Final"], axis=1), df[df["Time_Final"] == 1].drop(['Sweetener_SA', 'Sweetener_ST','Sweetener_SU', 'Sex_MAN', 'Sex_WOMAN', 'Time_Final', 'Time_Initial','numVol', 'Weight','BMI', 'Fat', 'CVRI', 'Bpmin', 'Bpmax', 'Frec'], axis = 1)
+        X, y = df[df["Time_Initial"] == 1].set_index("numVol").drop(["Time_Initial", "Time_Final"], axis=1), df[df["Time_Final"] == 1].set_index("numVol").drop(['Sweetener_SA', 'Sweetener_ST','Sweetener_SU', 'Sex_MAN', 'Sex_WOMAN', 'Time_Final', 'Time_Initial', 'Weight','BMI', 'Fat', 'CVRI', 'Bpmin', 'Bpmax', 'Frec'], axis = 1)
         
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=42)
     
 
     return(X_train, X_test, y_train, y_test)
\ No newline at end of file
diff --git a/scripts/__pycache__/main_retrain.cpython-311.pyc b/scripts/__pycache__/main_retrain.cpython-311.pyc
index 035f2f1..ab2f4c6 100644
Binary files a/scripts/__pycache__/main_retrain.cpython-311.pyc and b/scripts/__pycache__/main_retrain.cpython-311.pyc differ
diff --git a/scripts/__pycache__/utils.cpython-311.pyc b/scripts/__pycache__/utils.cpython-311.pyc
index 56f95da..546e623 100644
Binary files a/scripts/__pycache__/utils.cpython-311.pyc and b/scripts/__pycache__/utils.cpython-311.pyc differ
diff --git a/scripts/fitEnsemble.py b/scripts/fitEnsemble.py
new file mode 100644
index 0000000..1493222
--- /dev/null
+++ b/scripts/fitEnsemble.py
@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""
+@author: dres2
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pickle
+import re
+import os
+from utils import encodingSplitting
+from sklearn.model_selection import RepeatedKFold, cross_val_score, GridSearchCV
+
+from xgboost import XGBRFRegressor
+from lightgmb import LGBMRegressor
+import optuna
+
+paths = ["plasmFlav_ord.csv", "plasmAnt_ord.csv", "urineFlav_ord.csv", "urineAnt_ord.csv"]
+paths = ["../data/" + s for s in paths]
+
+def fitRF():
+    '''
+    function to train a Random Forest 
+    '''
+    
+    param_grid_light = {
+    'bootstrap': [False, True],
+    'max_depth': [80, 90, 100, 110],
+    'max_features': [0.2, 0.5, 0.7, 0.9, 2, 3],
+    'min_samples_leaf': [3, 4, 5],
+    'min_samples_split': [8, 10, 12],
+    'n_estimators': [100, 200, 300, 1000]
+    }
+    
+
+    print(" ----------------- SETTING UP TRAINING ----------------- ")
+    
+    X_train, X_test, y_train, y_test = encodingSplitting(df, full = full)
+    
+    model = RandomForestRegressor()
+    
+    cv = RepeatedKFold(n_splits=3, n_repeats=3, random_state=42)
+    
+    grid_search = GridSearchCV(estimator = model, param_grid = param_grid_light, cv = cv, n_jobs = -1, verbose = 2)
+
+    print(" ----------------- STARTING TRAINING ----------------- ")
+
+    grid_search.fit(X_train, y_train)
+
+    print(" ----------------- TRAINING ENDED ----------------- ")
+
+    if (full):    
+        filename = os.path.join("..", "models","RF_Full_5050"+df_name+".pkl")
+                
+    else: 
+        filename = os.path.join("..", "models","RF_Met_5050"+df_name+".pkl")
+    
+    best_model = grid_search.best_estimator_
+    
+    with open(filename, 'wb') as file:
+        pickle.dump(best_model, file)
+    
+    print(" ----------------- MODEL SAVED ----------------- ")
+
+    if (eval):
+        evaluateNPlot(filename, df, df_name, y_test=y_test, X_test=X_test, full = full,  pipeline = True,  plot = plot)
+    else:
+        print(" ----------------- ENDED SCRIPT ----------------- ")
+
+
+
+    print("lalala")
+
+fitRF()
\ No newline at end of file
diff --git a/scripts/main_retrain.py b/scripts/main_retrain.py
index 3bb662e..f66c230 100644
--- a/scripts/main_retrain.py
+++ b/scripts/main_retrain.py
@@ -14,13 +14,11 @@ from sklearn.ensemble import RandomForestRegressor
 
 sns.set_theme()
 
-
-
 def evaluateNPlot(modelPath, df, df_name, y_test ='' , X_test='', full = True,  pipeline = True,  plot = True):
    
    # extract model name  
-   
-    modelname = re.sub(".pkl","",re.sub("model+(RF|XGB|MLP)+(Met|Full)","", modelPath))
+    
+    modelname = re.sub(".pkl","",re.sub("../models/", "", modelPath)) # +(RF|XGB|MLP)+(Met_lightTrain|Full_lightTrain)","", modelPath))
 
     # define model type
     if re.match("Full", modelPath):
@@ -36,7 +34,7 @@ def evaluateNPlot(modelPath, df, df_name, y_test ='' , X_test='', full = True,
 
     # define the evaluation procedure
     
-    cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=42)
+    cv = RepeatedKFold(n_splits=3, n_repeats=3, random_state=42)
     
     # read or inherit data
     
@@ -52,28 +50,33 @@ def evaluateNPlot(modelPath, df, df_name, y_test ='' , X_test='', full = True,
         
     #X_test.to_csv("X_test_"+modelname+".csv") 
     
-    scores = pd.Series()
+    scores = []
+    mean_scores = []
     metabs = pd.Series(y_test.columns)
+    
     for n in metabs:
+        
         print(" ----------------- " + n + " MODEL EVALUATION ----------------- ")
 
         n_scores = cross_val_score(model, X_test, y_test[n], scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
         n_scores = np.absolute(n_scores)
-        scores._append(pd.Series(n_scores))
-        print('Full model ' + df_name + ' for metabolite '+n+ ' MAE: %.3f (%.3f)' % (np.mean(n_scores), np.std(n_scores)))
+        scores.append(n_scores) 
+        mean_scores.append(np.mean(n_scores))
+
+        print('Full model ' + modelname + ' for metabolite '+n+ ' MAE: %.3f (%.3f)' % (np.mean(n_scores), np.std(n_scores)))
     
     # full model evaluation
     print(" ----------------- MEAN PERFORMANCE EVALUATION ----------------- ")
 
-    n_scores = cross_val_score(model, X_test, y_test, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
-    n_scores = np.absolute(n_scores)
-    scores._append(pd.Series(n_scores))
-    metabs._append(pd.Series(["Mean Performance"]))
-    df_scores = pd.concat([metabs, scores])
-    df_scores.columns = ["Metabolite modeled", "MAE score"]
-    df_scores.to_csv("scores_" + df_name +".csv", sep = ",")
+    n_score_all = cross_val_score(model, X_test, y_test, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
+    n_score_all = np.absolute(n_score_all)
+    scores.append((n_score_all))
+    mean_scores.append(np.mean(n_score_all))
+    df_scores = pd.concat([metabs, pd.Series(scores), pd.Series(mean_scores)], axis = 1, ignore_index = True)
+    df_scores.columns = ["Metabolite modeled", "MAE score", "Mean MAE score"]
+    df_scores.to_csv("../results/predicts/scores_" + modelname +".csv", sep = ",")
     
-    print("Mean performance of all model" + modelname +  " MAE: %.3f (%.3f)" % (np.mean(n_scores), np.std(n_scores)))
+    print("Mean performance of all model" + modelname +  " MAE: %.3f (%.3f)" % (np.mean(n_score_all), np.std(n_score_all)))
     
     # plotting
     if (plot):
@@ -94,49 +97,52 @@ def evaluateNPlot(modelPath, df, df_name, y_test ='' , X_test='', full = True,
         df_predTest = pd.DataFrame([i for i in y_pred], index = y_test.index, columns= y_test.columns).add_suffix("_pred").join(y_test.add_suffix('_test')).fillna(0)
         df_predTest["Sex"] = dummies_sex
         df_predTest["Sweetener"] = dummies_sweetener
-        df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv("df_pred-test_"+df_name+".csv", sep = ",")
+        df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv("../results/predicts/df_pred-test_"+modelname+".csv", sep = ",")
             
         # print plots
         metabs = y_test.columns.drop(list(y_test.filter(regex='Sex|Sweetener'))) 
 
         print(" ----------------- STARTING PLOTTING ----------------- ")
 	
-	
+    	
         for metab in metabs:
             #sns.regplot(data = pred_prueba_DF, x=metab+"_pred", y=metab+"_test", )
             
             print(" ----------------- PREDICTED VS TEST " + metab + " ----------------- ")
 
-            plt.figure()
             
-            ax = plt.gca()
-            minPred = min(df_predTest[metab+"_pred"])
-            maxPred = max(df_predTest[metab+"_pred"])
-            minTest = min(df_predTest[metab+"_test"])
-            maxTest = max(df_predTest[metab+"_test"])
-            ax.set_xlim([min(minPred,  minTest),  max(maxPred,  maxTest)])
-            ax.set_ylim([min(minPred,  minTest),  max(maxPred,  maxTest)])
+
+            adjustAxis = True
+            if (adjustAxis):
+                ax = plt.gca()
+                minPred = min(df_predTest[metab+"_pred"])
+                maxPred = max(df_predTest[metab+"_pred"])
+                minTest = min(df_predTest[metab+"_test"])
+                maxTest = max(df_predTest[metab+"_test"])
+                ax.set_xlim([min(minPred,  minTest),  max(maxPred,  maxTest)])
+                ax.set_ylim([min(minPred,  minTest),  max(maxPred,  maxTest)])
+
             
+            plt.figure(figsize = (10,10))
             sns.lmplot(data = df_predTest, x=metab+"_pred", y=metab+"_test")
             plt.title("TEST VS PREDICTED lmplot " + metab + " " + modelname)
-            plt.savefig("testVsPredicted_lmplot_"+ metab+"_"+df_name+".png")
-            plt.close()
-           
-            '''
-            if (residPlot):
-	    	print(" ----------------- PREDICTED VS RESIDUALS " + metab + " ----------------- ")
-	    	plt.figure()
-	    	y_pred_metab = y_pred[metab]
-		residuals = y_test[metab]-y_pred[metab]
-		df_all = pd.concat([y_pred_metab, residuals], axis=1)
-		df_all.columns = ["Predicted", "Residuals"]
+            plt.savefig("../results/plots/testVsPredicted_lmplot_"+ metab+"_"+modelname+".png")
+            plt.close("all")   
+
+            print(" ----------------- PREDICTED VS RESIDUALS " + metab + " ----------------- ")
+
+            plt.figure(figsize = (10,10))
+            y_pred_metab = df_predTest[metab+"_pred"]
+            residuals = df_predTest[metab+"_test"]-df_predTest[metab+"_pred"]
+            df_all = pd.concat([y_pred_metab, residuals], axis=1)
+            df_all.columns = ["Predicted", "Residuals"]
+            
+            sns.scatterplot(data = df_all, x="Predicted", y="Residuals")
+            plt.title("RESIDUALS VS PREDICTED " + metab + " " + modelname)
+            plt.axhline(y=0)
+            plt.savefig("../results/plots/residualsVsPredicted_"+ metab+"_"+modelname+".png")
+            plt.close("all")   
 
-		sns.scatterplot(data = df_all, x="Predicted", y="Residuals")
-		plt.title("RESIDUALS VS PREDICTED " + metab + " " + modelname)
-		plt.savefig("residualsVsPredicted_"+ metab+"_"+df_name+".png")
-		plt.axhline(y=0)
-		plt.close()   
-	    '''
     
 def RF_Fit(df, df_name, full = True, eval = True,  plot = True):
     
@@ -166,10 +172,9 @@ def RF_Fit(df, df_name, full = True, eval = True,  plot = True):
     
     model = RandomForestRegressor()
     
-    cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=42)
+    cv = RepeatedKFold(n_splits=3, n_repeats=3, random_state=42)
     
-    grid_search = GridSearchCV(estimator = model, param_grid = param_grid_extensive, cv = cv, n_jobs = -1, verbose = 3)
-
+    grid_search = GridSearchCV(estimator = model, param_grid = param_grid_light, cv = cv, n_jobs = -1, verbose = 2)
 
     print(" ----------------- STARTING TRAINING ----------------- ")
 
@@ -178,9 +183,10 @@ def RF_Fit(df, df_name, full = True, eval = True,  plot = True):
     print(" ----------------- TRAINING ENDED ----------------- ")
 
     if (full):    
-        filename = os.path.join("..", "models","RF_Full_lightTrain"+df_name+".pkl")
+        filename = os.path.join("..", "models","RF_Full_5050"+df_name+".pkl")
+                
     else: 
-        filename = os.path.join("..", "models","RF_Full_lightTrain"+df_name+".pkl")
+        filename = os.path.join("..", "models","RF_Met_5050"+df_name+".pkl")
     
     best_model = grid_search.best_estimator_
     
@@ -190,7 +196,7 @@ def RF_Fit(df, df_name, full = True, eval = True,  plot = True):
     print(" ----------------- MODEL SAVED ----------------- ")
 
     if (eval):
-        evaluateNPlot(filename, df, df_name, y_test=y_test, X_test=X_test, full = True,  pipeline = True,  plot = True)
+        evaluateNPlot(filename, df, df_name, y_test=y_test, X_test=X_test, full = full,  pipeline = True,  plot = plot)
     else:
         print(" ----------------- ENDED SCRIPT ----------------- ")
 
diff --git a/scripts/modelSanoApp.py b/scripts/modelSanoApp.py
index 27f291c..3253e1e 100644
--- a/scripts/modelSanoApp.py
+++ b/scripts/modelSanoApp.py
@@ -4,10 +4,18 @@ Created on Wed May  8 21:27:35 2024
 
 @author: dres2
 """
+
 from main_retrain import evaluateNPlot, RF_Fit
 from utils import fullRead
+# from fitEnsemble import fitRF
 import re
 import pandas as pd
+import sys
+
+testeo = sys.argv[1]
+ejecucion = sys.argv[2]
+ejecucionPeque = sys.argv[3]
+
 
 print(" ----------------- PACKAGES EXPORTING ENDED ----------------- ")
 
@@ -18,29 +26,40 @@ paths = ["../data/" + s for s in paths]
 
 
 # execution
-test = True
-execution = False
-if (test):
+df, df_name = fullRead(paths[2], sep = ",", full = True)
+df_name = re.sub("../", "", df_name)
+df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric)
+RF_Fit(df, df_name, full = True, eval=True,  plot = True)
+
+if testeo == True:
+
+    print(" ----------------- TEST MODE INCIATING ----------------- ")
+
     df, df_name = fullRead(paths[1], sep = ",", full = True)
     df_name = re.sub("../", "", df_name)
     df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric)
     
     #y_test = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/X_metTest_RF_Met_lightTrainplasmFlav.csv") 
     #X_test = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/y_metTest_RF_Met_lightTrainplasmFlav.csv")
+    
     residPlot = False  
     evaluateNPlot(modelPath = "../models/RF_Full_lightTrainplasmAnt.pkl", df = df,  df_name = df_name,  pipeline = False)
 
-elif (execution): 
+else:
+    print(" ----------------- NO TESTING MODE ----------------- ")
+
+if ejecucion == True: 
+    
+    print(" ----------------- EXECUTION MODE INCIATING ----------------- ")
+    
     for path in paths:
         df, df_name = fullRead(path, sep = ",", full = True)
         df_name = re.sub("../", "", df_name)
         df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric)
 
-        RF_Fit(df, df_name, met = True, eval=True,  plot = True)
-        RF_Fit(df, df_name, met = False, eval=True,  plot = True)
-
+        RF_Fit(df, df_name, full = True, eval=True,  plot = True)
+        RF_Fit(df, df_name, full = False, eval=True,  plot = True)
 else:
-    df, df_name = fullRead(paths[1], sep = ",", full = True)
-    df_name = re.sub("../", "", df_name)
-    df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric)
-    RF_Fit(df, df_name, full = True, eval=True,  plot = True)
+    print(" ----------------- NO EXECUTION MODE ----------------- ")
+
+    
diff --git a/scripts/pruebas.ipynb b/scripts/pruebas.ipynb
index 31ac2e4..85c3365 100644
--- a/scripts/pruebas.ipynb
+++ b/scripts/pruebas.ipynb
@@ -2,155 +2,80 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "2aff3527-b25f-4f6c-b4f7-44109012489f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import re \n",
-    "import os\n",
-    "\n",
-    "\n",
-    "def fullRead(pathToTable, sep, full = False):\n",
-    "\n",
-    "  df_renamed = pd.read_csv(pathToTable, sep = sep, encoding = \"latin_1\")\n",
-    "    \n",
-    "  df_name = re.sub(\"_ord.csv\",\"\",(re.sub(\"data/\", \"\" ,pathToTable)))\n",
-    "  \n",
-    "\n",
-    "  print (\"Reading dataset \" + df_name)\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")\n",
-    "  \n",
-    "  # reading and merging    \n",
-    "  \n",
-    "  if (full):\n",
-    "    print (\"Reading and merging anthro data\")\n",
-    "    print (\"#\")\n",
-    "    print (\"#\")\n",
-    "    print (\"#\")  \n",
-    "   \n",
-    "    df_anthro = pd.read_csv( os.path.join(\"..\",\"data\",\"chronicAnthropometricCardiovascularData.csv\"), sep=\";\", decimal=\",\")\n",
-    "    df_renamed = df_renamed.merge(df_anthro)\n",
-    "\n",
-    "    # separating by time moment and renaming\n",
-    "\n",
-    "    df_renamed[\"Weight\"] = \"\"\n",
-    "    df_renamed[\"BMI\"] = \"\"\n",
-    "    df_renamed[\"Fat\"] = \"\"\n",
-    "    df_renamed[\"CVRI\"] = \"\"\n",
-    "    df_renamed[\"Bpmin\"] = \"\"\n",
-    "    df_renamed[\"Bpmax\"] = \"\"\n",
-    "    df_renamed[\"Frec\"] = \"\"\n",
-    "\n",
-    "    for i in range(len(df_renamed)):\n",
-    "\n",
-    "        if df_renamed.loc[i][\"Time\"] == \"Initial\":\n",
-    "            df_renamed.loc[i,\"Weight\"] = df_renamed.loc[i][\"Peso inicial\"]\n",
-    "            df_renamed.loc[i,\"BMI\"] = df_renamed.loc[i][\"IMC Inicial\"]\n",
-    "            df_renamed.loc[i,\"Fat\"] = df_renamed.loc[i][\"Grasa inicial\"]\n",
-    "            df_renamed.loc[i,\"CVRI\"] = df_renamed.loc[i][\"IRCV inicial\"] \n",
-    "            df_renamed.loc[i,\"Bpmin\"] = df_renamed.loc[i][\"Bpmin inicial\"] \n",
-    "            df_renamed.loc[i,\"Bpmax\"] = df_renamed.loc[i][\"Bpmax inicial\"] \n",
-    "            df_renamed.loc[i,\"Frec\"] = df_renamed.loc[i][\"Frec inicial\"] \n",
-    "                \n",
-    "        if df_renamed.loc[i][\"Time\"] == \"Final\":\n",
-    "        \n",
-    "            df_renamed.loc[i,\"Weight\"] = df_renamed.loc[i][\"Peso final\"]\n",
-    "            df_renamed.loc[i,\"BMI\"] = df_renamed.loc[i][\"IMC Final\"]\n",
-    "            df_renamed.loc[i,\"Fat\"] = df_renamed.loc[i][\"Grasa final\"]\n",
-    "            df_renamed.loc[i,\"CVRI\"] = df_renamed.loc[i][\"IRCV Final\"] \n",
-    "            df_renamed.loc[i,\"Bpmin\"] = df_renamed.loc[i][\"Bpmin final\"] \n",
-    "            df_renamed.loc[i,\"Bpmax\"] = df_renamed.loc[i][\"Bpmax final\"] \n",
-    "            df_renamed.loc[i,\"Frec\"] = df_renamed.loc[i][\"Frec final\"] \n",
-    "        \n",
-    "    df_renamed.drop(columns = [\"Peso inicial\", \"Peso final\", \"Delta Peso\", \"Talla\", \"IMC Inicial\", \"IMC Final\", \"Delta IMC\", \"Grasa inicial\", \"Grasa final\", \"Delta Grasa\", \"IRCV Final\", \"IRCV inicial\", \"Bpmin final\", \"Bpmin inicial\", \"Bpmax final\", \"Bpmax inicial\", \"Frec final\", \"Frec inicial\",], inplace=True )\n",
-    "  \n",
-    "  #   \n",
-    "  df_renamed.drop(columns = [\"Unnamed: 0\", \"grouping\"], inplace=True )\n",
-    "  \n",
-    "  print (\"Filling NA with zeros\")\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")\n",
-    "  print (\"#\")  \n",
-    "  df_renamed.fillna(0, inplace=True)\n",
-    "  \n",
-    "  return (df_renamed, df_name)\n",
-    "\n",
-    "def scaling(df_read):\n",
-    "   \n",
-    "   import numpy as np\n",
-    "   from sklearn import preprocessing\n",
-    "\n",
-    "   scaler = preprocessing.RobustScaler()\n",
-    "   numCols = df_read.select_dtypes(include=np.number).drop(\"numVol\", axis=1).columns\n",
-    "   df_read[numCols] = scaler.fit_transform(df_read[numCols])\n",
-    "   \n",
-    "   return (df_read)\n",
-    "\n",
-    "def encodingSplitting(df, full = True):\n",
-    "    \n",
-    "    from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "    \n",
-    "    df = pd.get_dummies(df, columns = [\"Sweetener\", \"Sex\", \"Time\"], drop_first=False)\n",
-    "    \n",
-    "    if (full):\n",
-    "        print (\"Split for anthropometric + metabolic\")\n",
-    "\n",
-    "        X, y= df[df[\"Time_Initial\"] == 1].set_index(\"numVol\").drop([\"Time_Initial\", \"Time_Final\"], axis=1), df[df[\"Time_Final\"] == 1].set_index(\"numVol\").drop(['Sweetener_SA', 'Sweetener_ST','Sweetener_SU','Time_Final', 'Time_Initial', 'Sex_MAN', 'Sex_WOMAN'], axis = 1)\n",
-    "        \n",
-    "    else:\n",
-    "        print (\"Split for only metabolic\")\n",
-    "        X, y = df[df[\"Time_Initial\"] == 1].set_index(\"numVol\").drop([\"Time_Initial\", \"Time_Final\"], axis=1), df[df[\"Time_Final\"] == 1].set_index(\"numVol\").drop(['Sweetener_SA', 'Sweetener_ST','Sweetener_SU', 'Sex_MAN', 'Sex_WOMAN', 'Time_Final', 'Time_Initial', 'Weight','BMI', 'Fat', 'CVRI', 'Bpmin', 'Bpmax', 'Frec'], axis = 1)\n",
-    "        \n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "    \n",
-    "\n",
-    "    return(X_train, X_test, y_train, y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "c3f8398f-1334-423b-b76a-80e9a1139e12",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['.git', '.ipynb_checkpoints', 'data', 'results', 'scripts']"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.listdir(\"../\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "f1563bc1-b29e-4838-ab88-0eab438f3d90",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 1,
+   "id": "0132bde1-6762-48e0-a2bf-73531f9cc934",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reading dataset ..\\data\\plasmAnt\n",
+      "XGBoost version: 2.0.3\n",
+      "LightGBM version: 4.3.0\n",
+      "Optuna version: 3.6.1\n",
+      "Scikit-learn version: 1.4.2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/die/eric_env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Tratamiento de datos\n",
+    "# ==============================================================================\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from utils import fullRead, scaling\n",
+    "# Gráficos\n",
+    "# ==============================================================================\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "# Modelado\n",
+    "# ==============================================================================\n",
+    "import xgboost\n",
+    "from xgboost import XGBRFRegressor\n",
+    "import lightgbm\n",
+    "from lightgbm import LGBMRegressor\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split, RepeatedKFold, cross_val_score\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.preprocessing import TargetEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "import sklearn\n",
+    "import optuna\n",
+    "import time\n",
+    "\n",
+    "# Configuración warnings\n",
+    "# ==============================================================================\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+    "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
+    "\n",
+    "print(f\"XGBoost version: {xgboost.__version__}\")\n",
+    "print(f\"LightGBM version: {lightgbm.__version__}\")\n",
+    "print(f\"Optuna version: {optuna.__version__}\")\n",
+    "print(f\"Scikit-learn version: {sklearn.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e30c566c-2fe7-4cff-bbfd-975b48611e95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading dataset urineFlav\n",
       "#\n",
       "#\n",
       "#\n",
@@ -164,567 +89,284 @@
       "#\n",
       "#\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(Index([ 19,  54,  56, 108,   5,  41,  72, 128,  32,  64,  62, 140,  11, 109,\n",
-       "        130,  12,  86,  65, 136,   1,  27,  53,  76, 117,  25],\n",
-       "       dtype='int64', name='numVol'),\n",
-       " Index([ 19,  54,  56, 108,   5,  41,  72, 128,  32,  64,  62, 140,  11, 109,\n",
-       "        130,  12,  86,  65, 136,   1,  27,  53,  76, 117,  25],\n",
-       "       dtype='int64', name='numVol'),\n",
-       " Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
-       "        ...\n",
-       "        133, 134, 135, 136, 137, 138, 139, 140, 141, 142],\n",
-       "       dtype='int64', name='numVol', length=122),\n",
-       " Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
-       "        ...\n",
-       "        133, 134, 135, 136, 137, 138, 139, 140, 141, 142],\n",
-       "       dtype='int64', name='numVol', length=122))"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "import os\n",
-    "from sklearn.model_selection import train_test_split\n",
+    "paths = [\"plasmFlav_ord.csv\", \"plasmAnt_ord.csv\", \"urineFlav_ord.csv\", \"urineAnt_ord.csv\"]\n",
+    "paths = [\"../data/\" + s for s in paths]\n",
     "\n",
-    "pathPlasmAnt = os.path.join(\"..\",\"data\", \"plasmAnt_ord.csv\")\n",
-    "df, df_name = fullRead(pathPlasmAnt, sep = \",\", full = True)\n",
-    "df = pd.get_dummies(df, columns = [\"Sweetener\", \"Sex\", \"Time\"], drop_first=False)\n",
-    "\n",
-    "X, y = df[df[\"Time_Initial\"] == 1].set_index(\"numVol\").drop([\"Time_Initial\", \"Time_Final\"], axis=1), df[df[\"Time_Final\"] == 1].set_index(\"numVol\").drop(['Sweetener_SA', 'Sweetener_ST','Sweetener_SU', 'Sex_MAN', 'Sex_WOMAN', 'Time_Final', 'Time_Initial', 'Weight','BMI', 'Fat', 'CVRI', 'Bpmin', 'Bpmax', 'Frec'], axis = 1)\n",
-    "\n",
-    "# X_train, X_test, y_train, y_test = encodingSplitting(df, full = True)\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "X_test.index,y_test.index, X.index, y.index"
+    "df, df_name = fullRead(paths[2], sep = \",\", full = True)\n",
+    "#Sweetener: object, Sex: object, Weight: object, BMI: object, Fat: object, CVRI: object, Bpmin: object, Bpmax: object, Frec: object\n",
+    "full = False\n",
+    "if not full:\n",
+    "    df[['Weight', 'BMI', \"Fat\", \"CVRI\", \"Bpmin\", \"Bpmax\", \"Frec\"]] = df[['Weight', 'BMI', \"Fat\", \"CVRI\", \"Bpmin\", \"Bpmax\", \"Frec\"]].apply(pd.to_numeric)\n",
+    "df.dropna(inplace = True)\n",
+    "df[['Sex', 'Sweetener']] = df[['Sex', 'Sweetener']].astype(\"category\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "id": "d8f5dd02-a95c-468d-a027-13ead18460d7",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 3,
+   "id": "834e8624-32b6-4a50-89d0-80194d71b5cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[df[\"numVol\"].duplicated(keep=False)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9262fea9-96be-4337-83ed-669ef354f51a",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "uwu\n",
-      "uwu\n"
+      "Observaciones en train: (37, 22)\n",
+      "Observaciones en validation: (10, 22)\n",
+      "Observaciones en test: (12, 22)\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(Index([ 19,  54,  56, 108,   5,  41,  72, 128,  32,  64,  62, 140,  11, 109,\n",
-       "        130,  12,  86,  65, 136,   1,  27,  53,  76, 117,  25],\n",
-       "       dtype='int64', name='numVol'),\n",
-       " Index([ 19,  54,  56, 108,   5,  41,  72, 128,  32,  64,  62, 140,  11, 109,\n",
-       "        130,  12,  86,  65, 136,   1,  27,  53,  76, 117,  25],\n",
-       "       dtype='int64', name='numVol'),\n",
-       " Index([ 19,  54,  56, 108,   5,  41,  72, 128,  32,  64,  62, 140,  11, 109,\n",
-       "        130,  12,  86,  65, 136,   1,  27,  53,  76, 117,  25],\n",
-       "       dtype='int64', name='numVol'))"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "import pickle\n",
+    "X = df[df[\"Time\"] == \"Initial\"].set_index(\"numVol\").drop([\"Time\"], axis=1) \n",
+    "y = df[df[\"Time\"] == \"Final\"].set_index(\"numVol\").drop([\"Time\", \"Sweetener\", \"Sex\"], axis = 1)\n",
     "\n",
-    "modelPath = os.path.join(\"..\",\"data\",\"RF_Met_lightTrainplasmAnt.pkl\")\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X,\n",
+    "    y,\n",
+    "    train_size = 0.8,\n",
+    "    random_state = 42,\n",
+    "    shuffle = True\n",
+    ")\n",
     "\n",
-    "modelname = re.sub(\".pkl\",\"\",re.sub(\"model+(RF|XGB|MLP)+(Met|Full)\",\"\", modelPath))\n",
+    "X_train, X_val, y_train, y_val = train_test_split(\n",
+    "    X_train,\n",
+    "    y_train,\n",
+    "    train_size   = 0.8,\n",
+    "    random_state = 42,\n",
+    "    shuffle      = True\n",
+    ")\n",
     "\n",
-    "# define model type\n",
-    "if re.match(\"Full\", modelPath):\n",
-    "    met = True\n",
-    "\n",
-    "# open the model\n",
-    "\n",
-    "with open (modelPath, 'rb') as f:\n",
-    "    model = pickle.load(f)\n",
-    "\n",
-    "print (\"uwu\")\n",
-    "df_pred = model.predict(X_test)\n",
-    "print (\"uwu\")\n",
-    "y_pred = pd.DataFrame([i for i in df_pred], index = y_test.index, columns= y_test.columns)\n",
-    "y_pred.index, y_test.index, X_test.index"
+    "print(\"Observaciones en train:\", X_train.shape)\n",
+    "print(\"Observaciones en validation:\", X_val.shape)\n",
+    "print(\"Observaciones en test:\", X_test.shape)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "b3e4308d-5ace-4fa0-8f81-cdda1d632654",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(Index([ 37,  91,  95, 179,   9,  80, 125, 215,  63, 111, 107, 239,  21, 181,\n",
-       "        219,  23, 153, 112, 231,   0,  53,  89, 133, 197,  49,  84, 211, 187,\n",
-       "         73, 200,  25,  31, 145,  45, 233, 183, 166],\n",
-       "       dtype='int64'),\n",
-       " Index([ 36,  90,  94, 178,   8,  81, 124, 214,  62, 110, 106, 238,  20, 180,\n",
-       "        218,  22, 152, 113, 230,   1,  52,  88, 132, 196,  48,  85, 210, 186,\n",
-       "         72, 201,  24,  30, 144,  44, 232, 182, 167],\n",
-       "       dtype='int64'))"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_test.index,y_test.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "id": "7b01c0d4-d972-4788-9daf-394cf89a70a8",
+   "execution_count": null,
+   "id": "8d593aa1-462b-47ba-8810-14efad33a19d",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "dummies_sweetener = pd.from_dummies(X_test[['Sweetener_SA', 'Sweetener_ST','Sweetener_SU']]).replace(\"Sweetener_\", \"\",regex=True)\n",
-    "dummies_sex = pd.from_dummies(X_test[['Sex_MAN', \"Sex_WOMAN\"]]).replace(\"Sex_\", \"\",regex=True)\n",
-    "\n",
-    "# make predict\n",
-    "y_pred = model.predict(X_test)\n",
-    "# pred_prueba_DF = pd.DataFrame(pred_prueba, index = X_test.index-1, columns=y_test.columns).add_suffix(\"_pred\").join(y_test.add_suffix('_test')).fillna(0) \n",
-    "df_predTest = pd.DataFrame([i for i in y_pred], index = y_test.index, columns= y_test.columns).add_suffix(\"_pred\").join(y_test.add_suffix('_test')).fillna(0)\n",
-    "df_predTest[\"Sex\"] = dummies_sex\n",
-    "df_predTest[\"Sweetener\"] = dummies_sweetener\n",
-    "df_predTest.reindex(sorted(df_predTest.columns), axis=1)\n",
-    "\n",
-    "y_pred = pd.DataFrame([i for i in df_pred], index = y_test.index, columns= y_test.columns)\n",
-    "# print plots\n",
-    "metabs = y_test.columns.drop(list(y_test.filter(regex='Sex|Sweetener'))) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "ccc4aba6-33be-453a-9e4a-30307e982009",
-   "metadata": {
-    "tags": []
-   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\dres2\\AppData\\Local\\Temp\\ipykernel_1768\\1638866629.py:7: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
-      "  plt.figure()\n"
+      "Best trial: 53. Best value: 2.25675:  55%|▌| 55/100 [09:00<10:58, 14"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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IzoO/1ceBHk9wYJmth9O1iIjGE4b0OBEQAm0ON0519sDjY181EdF4wJAeZ1xeP0529sDmZKuaiCjRMaTHISEE2rrdOMlWNRFRQmNIj2Pu3lZ1p9MT76oQEdEIMKTHOSEE2rs9ONnZA7fPH+/qEBHRMDCkk0Rwdy0XOro94Kw7IqLEwJBOIkIIdDjZqiYiShQM6STk8QXYqiYiSgAM6SR1dqva5WWrmohIiRjSSc7jC6DJ5kI7W9VERIrDkCYIIdDp9OBEB1vVRERKwpCmEK8/gFOdPWh1uBEIsFVNRBRvDGnqx97jxYmOHjjcvnhXhYgoqTGkaUC+QAAtdheabS74/FxalIgoHhjSNCinx4cTHT2wu7hhBxFRrKnjXQFSvoAQaO1yo9vtQ6ZJB42Kn+0ocQUCAgdP2dHu9CDDqMWsPDNkWYp3tYgGxJCmIevx+HGiowfpRg0sBg0kib/YKLFU1bZi86461LU44PULaFQSirNNuGVpMcpLMuNdPaJ+2CSiYenbsIPTtSjRVNW24q5tB3C4yY4UnRrZqTqk6NQ43NSFu7YdQFVta7yrSNRPVELa7/ejpqYGHR0d0TgdJYC+6VotXS74OV2LFC4QENi8qw4Otw+5Zj30GhVkWYJeo0KuWQeH24/Nu+o49ZAUZ0QhvW7dOjz99NMAggG9dOlSzJ8/H/n5+XjnnXeiWT9SOIfLhxMdTg4sI0U7eMqOuhYH0o3aft00kiQhzahBXYsDB0/Z41RDooGNKKRfeeUVzJ07FwDwxhtvoL6+Hp9++inWrVuHDRs2RLWCpHz+QHBg2SnurkUK1e70wOsX0EYY9KhTyfAGBNqdnhjXjGhwIwrp1tZW5ObmAgC2b9+Ob3zjG5g6dSpuuukmHDhwIKoVpMTh6t2zmuuAk9JkGLXQqCR4Isz5d/sD0MgSMozaGNeMaHAjCumcnBwcOnQIfr8fO3bswGWXXQYAcDqdUKlUUa0gJZaz1wF3erhiGSnDrDwzirNN6HB6+32ADP6b9aI424RZeeY41ZBoYCMK6RtvvBErV67E7NmzIUkSLr/8cgDABx98gOnTp0e1gpSYvP4Amm0unLZzxTKKP1mWcMvSYph0KjTb3ejx+hEICPR4/Wi2u2HSqXDL0mLOlybFkcQI70u+8soraGxsxDe+8Q1MmjQJAPD8888jLS0NK1asiGollc5ut8NiscBms8FsHt0n8aNnHFGqlXLIkoR0oxZmg5pzqymuwuZJBwQ0MudJk7KNOKTpCwzpodGoZFhNWhi1XEOH4ocrjlEiGfJvy8cee2zIJ7311ltHVBka3/pugRu0KmSkaKFTc/wCxZ4sS5gzyRLvahANyZBb0oWFhUM7oSTh6NGjo6pUomFLemRMejUyjFqouRY4EdGAhtySrq+vH8t6UBJyuHzodvth1quRZtRCxVuORERh2DlIcSWEgK3Hiy6XD2ncuIOIKMyIQ/rEiRN4/fXX0dDQAI8nfJWehx9+eNQVo+QS6N24w97jQ1qKBma9Jt5VIiKKuxGF9Ntvv42vfvWrKCwsxGeffYbZs2fj2LFjEEJg/vz50a4jJRFfIIDWLje6XD5kmXTQqtlfTUTJa0S/Ae+8807cfvvt+OSTT6DX67F161Y0NjZi6dKl+MY3vhHtOlIScnv9ONnZgw4uMTquBQICB07YsOvIGRw4YeMuVETnGNE86dTUVNTU1KC4uBjp6el4//33MWvWLOzfvx8rVqzAsWPHxqCqysXR3WNLo5KRlaqDXsMpW+NJ2MIifgGNiguLEJ1rRC3plJQUuN1uAEBeXh7q6upC32tt5cbpFF19e1dz447xo6q2FXdtO4DDTXak6NTITtUhRafG4aYu3LXtAKpq+XuECBhhn/TixYtRWVmJmTNn4uqrr8btt9+OAwcO4NVXX8XixYujXUciAECn04Nut4+t6gQXCAhs3lUHh9uHXLM+NJpfL6uQa5bRbHdj8646LC6yciUwSnojCumHH34YDkfwtuy9994Lh8OBl19+GSUlJXjkkUeiWkGis/W1qrkQSuI6eMqOuhYH0o3aftPtJElCmlGDuhYHDp6yc2UwSnojCumioqLQ341GIzZt2hS1ChENhcPlg9Pt58YdCajd6YHXL6CN8AFLp5JhCwi0Oz0Dfp8ombAZQgkrIATaut040dGDHo8/3tWhIcowaqFRSfBE2MLU7Q9AI0vIMGpjXDMi5RlRSMuyDJVKFfFBFEtefwBNth6ctrvg5d7Vijcrz4zibBM6nN5+AwGFEOh0elGcbcKsvNHNlCAaD0Z0u3vbtm1hX3u9XlRXV+P555/HfffdF5WKEQ1Xt9sHp8ePNIMGaUYuL6pUsizhlqXFuGvbATTb3UgzaqBTyXD7A+h0emHSqXDL0mIOGiNClPeTfvHFF/Hyyy/jtddei9YpEwLnSSuPRiUjI0WLFB2Xp1eqsHnSAQGNzHnSROeKakjX1dXhwgsvRHd3d7ROmRAY0spl0KpgTeHyokoVCAgcPGVHu9ODDKMWs/LMbEETnSVqzYyenh48/vjjmDRpUrROSTRqPR4/Tnp7YNarkW7UMgAURpYlTrMiGsSIQjo9PT2sv08Iga6uLhiNRvzpT3+KWuWIoqFvO8xutx9ZqToYtBzcSESJYUQh/cgjj4SFtCzLyMrKwqJFi5Cenh61yhFFky8QHAWekaJFGqf3EFECiGqfdLJin3TiMWhVyE7VQ8Xb30SkYENuSX/88cdDPumFF144osoQxUqPx48THU6kGbUw67liGREp05BD+qKLLoIkSaHFBwb7peb3c/UnUj5/QKDN4Ya9xwurSQujltO1iEhZhjwvpb6+HkePHkV9fT1effVVFBYWYtOmTaiurkZ1dTU2bdqE4uJibN26dSzrSxR1Xn8AzTYXmm1csYyIlGVEfdILFy7Evffei6uuuirs+Pbt2/Gzn/0M+/bti1oFEwH7pMcPSZK4YhkRKcaIVng4cOAACgsL+x0vLCzEoUOHRl0pongRQqDD6eGmHUSkCCMK6RkzZuCXv/wlXC5X6Jjb7cYvf/lLzJgxI2qVI4qXvk07Wh3ufptAEBHFyohGyjz55JP413/9V+Tn52Pu3LkAgP3790OSJPztb3+LagWJ4sne40WPJ7gIil7DRVCIKLZGPE/a6XTiT3/6Ez799FMIITBz5kysXr0aKSkp0a6j4rFPOjmkG7XsqyaimOJiJlHAkE4eOo0K2ak6aFTcsIOIxt6Qb3e//vrrWL58OTQaDV5//fVBy371q18ddcWIlMjt9eNkRw/SjVqYDVwEhYjG1pBb0rIso7m5GdnZ2ZDlyK0ISZKSbjETtqSTk0YlIz1FCxP3rE5K3GaTYmHIv10CgcCAfydKVl5/AC12F+waFTJN3LM6mVTVtmLzrjrUtTjg9QtoVBKKs024ZWkxyksy4109Gkei9luls7MzWqciSigurx8nO3tgc3rjXRWKgaraVty17QAON9mRolMjO1WHFJ0ah5u6cNe2A6iqbY13FWkcGVFIP/TQQ3j55ZdDX3/jG99ARkYGJk6ciP3790etckSJQgiBtm43Tnb2wOPjnabxKhAQ2LyrDg63D7lmPfQaFWRZgl6jQq5ZB4fbj8276hAIcDwuRceIQnrLli3Iz88HAOzcuRNvvfUWduzYgeXLl+M//uM/olpBokTiZqt6XDt4yo66FgfSjdp+gwYlSUKaUYO6FgcOnrLHqYY03oxoxEtTU1MopP/2t79h5cqVWLZsGaZMmYJFixZFtYJEiaavVe30+pBl0kHN6VrjRrvTA69fQBvhZ6pTybAFBNqdnhjXjMarEf32SE9PR2NjIwBgx44duOyyywAEfzkl28huokiCe1YHW9VcjmB8yDBqoVFJ8ETYLc3tD0AjS8gwamNcMxqvRhTSX/va17B69WpcfvnlaGtrw/LlywEANTU1KCkpiWoFz7Vp0yYUFhZCr9ejtLQU77333qDld+3ahdLSUuj1ehQVFeHJJ5/sV2br1q2YOXMmdDodZs6ciW3bto1V9SnJBHpb1Sc6etDt9sW7OjRKs/LMKM42oWOAD15CCHQ6vSjONmFW3uimYhL1GdHt7kceeQRTpkxBY2Mjfv3rX8NkMgEI3gb/wQ9+ENUKnu3ll1/GunXrsGnTJlRUVGDLli1Yvnw5Dh06hMmTJ/crX19fj6uuugo333wz/vSnP6GyshI/+MEPkJWVha9//esAgN27d2PVqlX4xS9+geuuuw7btm3DypUr8f777w/71r3T44PaM7pfxD1e3okYj3q8fthdXhg0KmSkaDldK4HdWD4F975xEE02FyxGDXSyDHcgAJvTixSdCjeWT4HLx//HdH5G7fkjOKGWBV20aBHmz5+PzZs3h47NmDED1157LTZu3Niv/E9+8hO8/vrrOHz4cOjY2rVrsX//fuzevRsAsGrVKtjtdvz9738PlbnyyiuRnp6Ol156acB6uN1uuN3u0Nd2ux35+fnIX/dXyDrjqN8nERGNf8cevPq8ZUb8cf6FF17Al770JeTl5eH48eMAgEcffRSvvfbaSE85KI/Hg3379mHZsmVhx5ctW4aqqqoBn7N79+5+5a+44gp89NFH8Hq9g5aJdE4A2LhxIywWS+jRN4iOiIgomkZ0u3vz5s34+c9/jnXr1uFXv/pVaLBYWloaHn30UaxYsSKqlQSA1tZW+P1+5OTkhB3PyclBc3PzgM9pbm4esLzP50NraysmTJgQsUykcwLAnXfeifXr14e+7mtJ793wlVEvC1rf2j2q51PiSdGqkZGi4ShwIupnRCH9+OOP46mnnsK1116LBx98MHR8wYIFuOOOO6JWuYGcOzdRCDHoJgcDlT/3+HDPqdPpoNPp+h03atVD6mMYjOE8exYHhEDt6W7YXB5Y9FqU5KRA5iYPCS04uMyLdKMGFgO3wiSiL4woUerr6zFv3rx+x3U6Hbq7x6YlmJmZCZVK1a+F29LS0q8l3Cc3N3fA8mq1GlarddAykc4ZT9UNHXhxbyMa27rhDQhoZAn51hSsXpiPeZPT4109GgUhBNq7Pehy+ZCRokUKN+0gIoywT7qwsBA1NTX9jv/973/HjBkzRlunAWm1WpSWlmLnzp1hx3fu3Iny8vIBn1NWVtav/JtvvokFCxZAo9EMWibSOcfKwVM2XPHIu3j6/Xp82mzvN72juqEDD+88gqNnHDBo1bCmaGHQqnH0jAMP7zyC6oaOmNaXxobXH8BpuwtNth64OUKYKOmN6OP6f/zHf+Df//3f4XK5IITA3r178dJLL+GBBx7A008/He06hqxfvx5r1qzBggULUFZWht///vdoaGjA2rVrAQT7ik+ePIk//vGPAIIjuX/3u99h/fr1uPnmm7F79248/fTTYaO2b7vtNlxyySV46KGHsGLFCrz22mt466238P7774/Z+xjIzkOn8dnpLnx2ugt//qABVpMW5cVWVBRn4sJJFry4txFOjx+ZJi0kBG+H6tQSMk1atDo8eHFvI+bmp/HW9zjR4/HjpKcHqXoNMlK0UHELRKKkNKKQvvHGG+Hz+fDjH/8YTqcTq1evxsSJE/H4449jyZIl0a5jyKpVq9DW1ob7778fTU1NmD17NrZv346CggIAwXnaDQ0NofKFhYXYvn07fvSjH+GJJ55AXl4eHnvssdAcaQAoLy/HX/7yF9x999342c9+huLiYrz88ssxX95056HTYV+3OTx4Y38T3tjfBL1ahgBg0qkRCABnjy+SICFVr0FjWzdqT3djaq4ppvWmsdXl8qLb7UO6UQuzQc3+aqIkM+p50q2trQgEAvD7/XjggQfwhz/8AT09PdGqX0Kw2+2wWCyw2WwjHt19rLUbOw+dxuv7T+HgKRsG20THoFHBpFPBpFNDo5KDA4+cHvz0yum4eErGCN8FKZ1WLSMrVQedevDBhUQ0fgyrT7qzsxM33HADsrKyQq3SjIwMPPHEEygpKcGePXvwzDPPjFVdx7UpmSm4+ZIi/Pb6i/DK2jL8+IppqCixQj/AylQ9Xj/OODyob3PiWLsTLV1uICBg1mniUHOKFY8vgFOdLu6wRZREhtWS/sEPfoA33ngDq1atwo4dO3D48GFcccUVcLlcuOeee7B06dKxrKtiRaMl3efoGUfY126vHx8d78Cjb3+OdocHg/2wskxalBdnorzEiovy06DhvNtxy6BVIdOk48+YaJwbVkgXFBTg6aefxmWXXYajR4+ipKQEt956Kx599NExrKLyjWVI96lu6MB/vfkZulw+SJKEHq8fXn/kH51Rq8LCKRmoKLFiUaEVJj2n9Iw3kiRxbjXRODeskNZoNDh+/Djy8vIAAEajEXv37sXs2bPHrIKJIBYhDZwzT1oISAIwaNXQqCQcb3NGbGWrZAlzJ1lQXpyJihIrcsz6UdWRlEWjCvZV68+zEA4RJZ5hhXTfYiJZWVkAgNTUVHz88ccoLCwcswomgliFNBB5xbEOpwd76tpQWdeGfcc74PYNvN8tAJRkmVBeYkVFsRUl2Sa2wsYJk14Na4qO07WIxpFhhbQsy1i+fHloScw33ngDX/7yl5GSkhJW7tVXX41uLRUuliE9FC6vH/uOd6Cytg17jrahsyfyQKPsVF1wPnZJJuZOsnD96ASnkiWkp2hh1nMQIdF4MKyQvvHGG4dU7tlnnx1xhRKR0kL6bP6AwKFTdlTWtaKqrg0nOiJPj0vRqbCo0IryYisWFmbAxKUpE5ZOo0KmScvpWkQJLqH2k1YqJYf02YQQaGh3oqquDZW1bTjcZI/Yj62WJVyUn4by4mBoZ7MfOyFZDBqkG7WQeQucKCExpKMgUUL6XO3dHuyua0NlXSv2He8YdLT4BdkmVJQElyktykphP3YCUcsy0lI0SNVxxTKiRMOQjoJEDemz9Xj9+OhYB6rqWrG7rg12ly9i2VyzPtjCLrHiwonsx04UalmGxaiBWc+wJkoUDOkoGA8hfTZ/QOCTUzZU1QZb2ac6XRHLmnRqLC7KQHlxJi6eks4tFhOAWpZhNqiRqtdwJDiRwjGko2C8hfTZhBA41uYM3RY/3NQVsaxG1dePnYnyYiuyUnUxrCkNlyRJMOnUsBg00A6w/CwRxR9DOgrGc0ifq83hxu6jwYFn/2wYvB97Wk5qaD52YSb7sZXMpFMjzahlWBMpDEM6CpIppM/W4/Hjw2PtqKxrwwdHB+/HnmDRo6zYii+VZGLORAtvsyqUSadGeoqWa4ITKQRDOgqSNaTP5g8IHDhpQ1VdKypr29Bki9yPbdarsago2MK+eEoGDFrO5VUSSZJgMWiQZtBw6hZRnDGko4AhHa6vH7uythWVdW34rHnwfuz5k9NRUWJFWZEVVhP7sZWib+oWVy8jih+GdBQwpAd3pivYj11V24rqxs5B+7FnTEjtXUAlE1OsRvZjK4BGJSM9RcsV6IjigCEdBQzpoet2+/Bh73zsPUfb4XBH7sfOS9Ojond/7Nl57MeON61aRkaKFkYtw5ooVhjSUcCQHhmfP4CPT34xH/u03R2xrFmvRllvC3vBlHQYuC1j3Og0KmQYtRxLQBQDDOkoYEiPnhACR890o7J34NnnLZGvg1YtY/7kNFQUZ6Ks2IqMFG0Ma0p9DFoV0o1a7mNNNIYY0lHAkI6+FrsrNB+7prETvsDA/0wlADMmmFFREtwIZHIG+7FjLUWnRppRwx23iMYAQzoKGNJjy+H24cP63vnY9W3odvsjlp2Ubgjuj12ciZl5ZvZjx5BJr0aGUcu13ImiiCEdBQzp2PH6A/j4hA2VtcH9sVu6Ivdjpxk0WFxkRUWJFaUF6bwtGwOSJCHNoEGaUcM7GkRRwJCOAoZ0fAghUHemG+/XtqKqtg21g1w7nVpGaUE6KoqtWFxsRbqR/dhjSS3LyDBx2hbRaDGko4AhrQzNdhd21wXnY9ecsME/SD/2rDwzyksyQ/3YNDaMWjWsJi4zSjRSDOkoYEgrj8Plwwf17aiqa8Xe+nZ0eyL3Y+enG1DRG9gz88yQeZs2qiRJQrpRA4uBt8CJhoshHQUMaWXz+gOoaexEVW0bquracMYRuR873ahBWZEV5SVWlE5Oh4792FGjUcnINOk4v5poGBjSUcCQThxCCHze4ggtoFJ3pjtiWZ1axoKCdJSXZKKsKANp7MeOihSdGhncaYtoSBjSUcCQTlzNNheq6lrxfm0bPj7RiQjd2JCl3n7s4kxUlFgxKZ392KPBnbaIhoYhHQUM6fHB3uPF3mPtqKxtw976dvR4I/djF2QYUV4SnI89fUIq+7FHSCVLSDNoYTao2V9NNACGdBQwpMcfj6+3H7sueFu8zeGJWDbdqEFZsRVfKsnE/Mnp0Kp5G3e4NCoZFqMGqTqGNdHZGNJRwJAe34QQOHLagcq64Hzso62R+7H1GhkXT8lARbEVi4qssBi4F/NwaFQy0owapHIPayIADOmoYEgnl1OdPaiqa0NVXSs+PmEbtB97zkRLcH/skkxMTDPEtqIJjHtYEwUxpKOAIZ28bD3e4Hzs2lbsPdYOlzcQsewUqzE0H3taLvuxh0KjCu5hncKwpiTFkI4ChjQBwX7sfzZ09Lay29DeHbkf25qiRXmxFWXFVvZjD4FRG5y2xetEyYYhHQUM6fMLCIHa092wuTyw6LUoyUkZ1y3JgBD4rLkrtBHIsTZnxLIGjQoXTwnOx15cmAEz+7EHxGlblIwY0lHAkB5cdUMHXtzbiMa2bngDAhpZQr41BasX5mPe5PR4Vy8mTnb0oKquFZV1bfjk5OD92BdOSgtut1lixQQL+7HPJUkSUnQqmPUa7mxG4x5DOgoY0pFVN3Tg4Z1H4PT4YdZroFFJ8PoF7C4vjFoV1l8+NWmCuo/N6cWe+jZU1rbho2PtcPki92MXZaaE5mNPzTFxetI5NCoZJp0aKTo1b4XTuMSQjgKG9MACQuAnWw/g6BkHMk1aSPgiYAQEWh0eFGWZ8NDX54zrW9+DcXv9+GdDZ2i0eIfTG7FspkkbWvFs7qQ0htI5tGoZqToNUvVq3g6ncYMhHQUM6YEdaXbg568dgEGrhm6AQHH5AnB5fLh/xRxMzTXFoYbKEhACnzZ1obKuFZW1bWhoj9yPbdSqgvOxS6xYVJjBecVnkSUJZoMGZr0aaq4PTgmO8xpozNhcHngDAmbVwK0arUpClxCwuSKPgk4msiRhZp4ZM/PMuHlJERrbnaEW9sFT9rB+bKfHj11HzmDXkTNQyRIunGRBRe987FyzPn5vQgECQqDT6YGtxwuDRgW9RoZeo2L/NSUkhjSNGYteC40c7IPWqfsHtccvoJEkWPTcXWog+RlGrMowYtXF+eh0erD7aHA+9kfHO+A+qx/bHxCobuhEdUMnfve/dSjOSkFFcSbKS6y4IDt5+7GFEHB6fHD2fgZUyRJMOjXMBg134KKEwdvdUcDb3QNjn/TYcHn92Hc8OB97d10bOnsi92Nnp+pQVmxFRbEVc/PTGE69jFo1LAYN97YmxWNIRwFDOrKzR3en6jXQqiR4/AJdQxjdnWxzq0fCHxA43GRHZW1weteJjp6IZVO0KiwszEB5cSYWFWbApOeNNJ1GBYtBw+VHSbEY0lHAkB5c2DxpEbzFfb550pxbPTIN7U5U9Qb2oVN2RPrPrZIlXDTJgvKSTFQUW5Gd5P3YGpWMFJ061IedrF0EpDwM6ShgSJ/fcFrFnFsdHe3dHuw5GpyPva+hA55B5mOXZJtQUWxFRUkmirNSkjqkJEmCXiNDq5KhVcvQqVWc7kZxw5COAoZ09LAfe2z0eP3Yd6wDlXWt2HO0Hbbz9GNX9LawL5xk4TQmBEfeG7Sq4EOjYt8+xQxDOgoY0tHDudVjzx8QOHTKHtwf+zz92CadGosKM1BebMXCwgzuRtVLp1EhVa+GScuFU2hs8X8cKQrnVo89lSxhziQL5kyy4PuXFKGh3YnK2uB87ENNXWFlHW4f3v60BW9/2gK1LOGi/DRUlFhRXpyJrFRdnN5B/Lm9fri9frRJHqToVEjVcaQ4jQ2GNCkK51bHliRJKLCmoMCagtWLJqO92xNaQGXf8Q54/V/caPMFBD463oGPjnfgt2/XYmqOKTQfuygzOfuxhRBwuHxwuHxQyzJMejVMXEecooi3u6OAt7ujh33SytHj9eOjYx2oqmvF7ro22F2+iGVzzfrejUCsmDOR/dh9o8VTdCro1Gxh08gxpKOAIR1do5lbTWPDHxD45KQttK54k80VsWyqPtiPXVGSiYunpMOoTe4bdipZCi5LqlZBp5GhU3OKFw0dQzoKGNLRN5K51RQbQggca3OisjY48OzT5q6IZTUqCfMmp6Oi2IqyYisyTYnXjx3tRXUkSYJOHVxPXKuWoZIkyDKgkiSoZIkBTmEY0lHAkB4bXHEsMbQ63Nhd14bKujZUN4T3Y59rem4qynvnY0+xGhUfSPFYVEfuDWu1SoJOrQoFuoqjyMdUICAQEAIBEfzdI3r/9AsBEUDv94L/tq0x/LDJkI4ChjRRkNPjw4fHOlBZ24oP6tvRNUg/9gSLHhUlVlQUZ2L2RIviQmg0i+qMxQdMjUqGWiVBLQcXWtGoJWhV8oj6/88NpL6/CyGC51fLivt5AMF6C3wRogLBPwGEfS0QfC+i7/jZoRsIBm9AfHEd/IGhx6BKDg62jBWGdBQwpIn68/kDOHDShsrejUAG68c269VYXGRFeYkVFxdkxH0602gGMMa69a2SJWhU8hfBdM5vdEnqC6rw1uD5nB3WfQ9ZQnDL1AHCPSAAWe69EyBJX7xub72CdQleyXO/1xe8CB3rDWL0BrNAsEWrgLhiSCcghjTR4IQQqG/tRmVdGyprW3HkdOR/5xqVhPmT00PzsTNSYj/dbqSL6iit9U3RF+uQTu5hl0QUE5IkoSjLhKIsE9YsLsCZLndoPnZ1Qyd8Z91u9PoFPqhvxwf17Xh45+eYMSE1NB+7ICM2/dgjWVQnIARe3NsIp8cf1vrWqSVkmrRodXjw4t5GzM1Pi3vrmxIHQ5qIYi4rVYcVF+VhxUV56Hb78OGxdlTWtmFPfRu63f6wsoebunC4qQt/eL8eE9MMvQPPrJiVN3b92CNZVKf2dDca27ph1mvCbo8DgAQJqXoNGtu6UXu6e9DWt7m39X30jAMP7zzCKYdJjiFNRHGVolPj0mnZuHRaNnz+AD4+YQvdFm/pcoeVPdnZg//edwL/ve8ELAYNFhdloKI4E6VT0mHQRK8fuyQnBfnWlIh90l0uL4qyTCjJ+eK2Z6xb35QcGNJEpBhqlYz5BemYX5COH/5LMY6e6Q4toPJ5S3g/tq3Hi38cPI1/HDwNrVpG6eR0lPfOxx5tP7YsSVi9MB8P7zyCVodnwEV1Vi/MDwvOWLa+KXkwpIlIkSRJQnG2CcXZJnyrbApa7C5U9c7HrmnsDJs24/EFsPtoG3YfbYO0E5gxwRyc3lWSickZxhG9/rzJ6Vh/+dRQX3FX76I6RVmmAfuKY9X6puTCkCaiIYn36ONssx7XzpuIa+dNhMPtw976dlTWtmLvsfawfmwB4FCTHYea7HjqvXpMSjegoncBlRkTzMPqx543OR1z89OG9L5j1foeC/H+2VJknIIVBZyCReOdkkcfe/0B7G/sDM3HPrcf+2xpBg3Kiq0oL7aitCAd+ij2Y/cZzpK2SthQRsk/WyXiPOkExJCm8Ww0c39jTQiB2hYHKuvaUFXbhtpB/j/p1DIWFAT7sRcXW5FujF5rdTgt03huKJNIP1uliHVIJ8x+ch0dHVizZg0sFgssFgvWrFmDzs7OQZ8jhMC9996LvLw8GAwGXHrppTh48GBYmUsvvTS4Cs5Zj+uvv34M3wlR4jh39LFOLUPu3SAi06SF0+PHi3sbh7yK1ViTJAkX5KTiO+VT8PtvleLFmxfhh/9SgvmT0/rd5nb7Aqisa8N/vnkE/2fzbtz6UjX+8mEjGtudo66HLEmYmmvCxVMyMDXXNGgruK/vuyjLBJfHhzanBy6PD0VZpiGHZEAIHGl24MNj7TjS7BjSzyPRfrbJKmFa0suXL8eJEyfw+9//HgDwve99D1OmTMEbb7wR8TkPPfQQfvWrX+G5557D1KlT8ctf/hLvvvsuPvvsM6SmpgIIhvTUqVNx//33h55nMBhgsViGXDe2pGm8GunKW0rU5fL29mO3Ye+xdjg9/ohlJ2cYQ/OxZ0wwx6R/dqT9wiO9XT2efraxxBXHBnD48GHs2LEDe/bswaJFiwAATz31FMrKyvDZZ59h2rRp/Z4jhMCjjz6KDRs24Gtf+xoA4Pnnn0dOTg5efPFFfP/73w+VNRqNyM3Njc2bIUog42n0capeg6/MyMFXZuTA4wtg/4lOVNW2obKuFa2O8Po3tDvR0O7EXz5sRLox2I9dUZyJ+ZPToBuDfmzgi9b3cIxmIZTx9LMdzxLidvfu3bthsVhCAQ0AixcvhsViQVVV1YDPqa+vR3NzM5YtWxY6ptPpsHTp0n7P+fOf/4zMzEzMmjULd9xxB7q6Iu+PCwButxt2uz3sQTQenT36eCCxGn0cbVq1jIunZOC2yy7Ay99bjM03zMeaxZNRlNW/hdTh9GL7gWZs+J9PcN2mKvz8tYP4x8Fm2JzeONT8C6O9XT1ef7bjTUK0pJubm5Gdnd3veHZ2NpqbmyM+BwBycnLCjufk5OD48eOhr2+44QYUFhYiNzcXn3zyCe68807s378fO3fujFifjRs34r777hvJWyFKKCOZ+5toJEnCtNxUTMtNxY0VhWiy9QTnY9e24eMTnTh7F0OXL4D3a1vxfm0rZAmYlWcJbbc5Md0Q03qPdiGUZPjZjgdxDel77733vGH34YcfAsCAi+oLIc672P653z/3OTfffHPo77Nnz8YFF1yABQsW4J///Cfmz58/4DnvvPNOrF+/PvS13W5Hfn7+oPUgSkQjmfub6CZYDPj6/En4+vxJsPd48UF9OyrrWvFhfQd6vF/0YwcEcOCkDQdO2vDkrqMosBpD87Gn5aaO+TUZ7e3qZPzZJqK4hvQPf/jD846knjJlCj7++GOcPn263/fOnDnTr6Xcp6+Pubm5GRMmTAgdb2lpifgcAJg/fz40Gg0+//zziCGt0+mg0+kGrTfReDHclbfGE7NBg8tn5uDymcF+7JrGTlTWtaKqrg1t5/RjH29z4nibEy/ubYQ1RRuajz1/cjq0AwzMGq1oLISSzD/bkQgIgdpmB461OZFh1GJWnhnyGG3y0ieuIZ2ZmYnMzMzzlisrK4PNZsPevXuxcOFCAMAHH3wAm82G8vLyAZ/Tdwt7586dmDdvHgDA4/Fg165deOihhyK+1sGDB+H1esOCnSjZDWflrfFKq5axsDADCwszcNtXBI6c7kJlbRuq6tpQ39odVrat24O/fdyEv33cBL0m2P9dUWzFoiIrLAZNVOoTrdvV/NkOzdmj6AUkaFTBZWtvWVqM8pLz59hIJdQUrFOnTmHLli0AglOwCgoKwqZgTZ8+HRs3bsR1110HIDgFa+PGjXj22WdxwQUX4IEHHsA777wTmoJVV1eHP//5z7jqqquQmZmJQ4cO4fbbb4fBYMCHH34IlWpoozg5BUu5uNwhxcLJzmA/dlVtKw6ctIX1Y59NloA5Ey0oL8lERbEVeWmj68eO50IoyeTcUfQmnRoefwAdTi9MOhUeuG7OmAV1woR0e3s7br31Vrz++usAgK9+9av43e9+h7S0tFAZSZLw7LPP4jvf+Q6AYP/zfffdhy1btqCjowOLFi3CE088gdmzZwMAGhsb8c1vfhOffPIJHA4H8vPzcfXVV+Oee+5BRkbGkOvGkFYmLndI8WDr8eKDo8GNQD481g6XNxCxbGFmSmg+9tSckfVjD2cZUhq+fku3ShJ06mADTgiBZrsbMyak4vkbF47Jre+ECWklY0grD5c7JCXw+AL4Z0NHsJVd14b27shzjq0mbTCwizNxUX7asPqxecdo7PRb9EVCKKQBoMfrh9Ptw5Y1CzBn0tAXwRqqhJiCRTQc584f7eur06klZJq0aHV48OLeRszNT+MvMhpTWrWMxUVWLC6yYt1lAp81d6GythWVdW043ha+/Gibw4M39jfhjf1NMGhUuLgwHRXFmVhclIFU/eD92CNZCIWG5nyj6HUqGbaAQLtzbBZ9YUjTuDPa+aNEY0GWJMyYYMaMCWZ8d0kRTnQ4Q/OxD54K78fu8frx7pFWvHskOB/7wklpofnYuRZ9/N5EEjrfKHq3PwCNLCEjihu0nI0hTeMOlzukRDAp3YiVC4xYuSAfnU4P9hwNzsfed6wDLt8X/dgBAdQ0dqKmsRNP/G8dirJSQvOxL8g2nXetCBqdQUfRC4FOpxczJqRiVt7oujojYUjTuBON+aNEsZRm1OLK2bm4cnYu3F4/9jV0oKq2DbuPtqHjnOVHj57pxtEz3XhhTwOyTDqUF1tRXmLFRflp0KgSYqXnhHLuoi9mvQYaWYbbH0Bn7+juW5YWj9l8aQ4ciwIOHFOWfqMxz5k/2urwoCjLhIe+Pidh+qRVsgSVLGGw/62SFJzhIEuAEIA/IBAQAv5I84FI8QJC4HCTPTQfu2GQbTRTtCosLMxAeXEmFhVmwKRnGyyawuZJSxI0MudJJwyGtPJEc/6oJAVjXpYkyHLvn71h2BeKKlkK/b3v+1Lo71+co+8zgRCAQPB2WUAEPzyc/T+x7+8qWYJalkb1KV0IAV8gGNZef6A3vIMBEOitSF8dAiI4l1clSaEPBuf26/t7gz8ggufz+QX3HI6RxnYnKnvnYx88ZUekq66SJcydZEF5cSYqSqzIMbMfOxoCQuDomW7oNKqYrTjGkI4ChvTwnB1mACDLvUGGswJNQiga+vrcvvi698+zw0P64nhf2Q+OtuHpynrUn+mGLyCgloPLHX73S4VYXGwNO0ffc88NX/b3DY3PH4BvgBa7OCv8g18H/yIAeH0BeP3BoPf6I88lpoF1OD3YUxecj/3R8Q54fJGvYUmWCeUlVlQUW1HCfuxRifV+0gzpKEjkkJYkCapB/sOGwks+qzUpATi7xdh7XApOIQxrRUpnhW+wJRrbXw6BgMDBU3a0Oz0x++RLw9fX2vf5BbyBAPx+Ab8QCASCf4bdeQgAvgBD/Wwurx/7jnegsrcf29YTeRvN7FRd7/7YVsxlP/awMaQTkJJCWj7rNmXwFmzw1mUoUHtv16p7b6Oq+R+UEtDZoe72+eHyBuD2+dn/juBYhEOn7KiqC87HPtHRE7Fsik6FhVMy8KWSTCwszECKjv3Y58OQTkDRDOlOpwcSvmiVDibUNyp/0YfI21iUzNw+P9y+AFxeP9xe3kYHgIY2JyrrWlFZ24bDTZH7sdWyhIvy04KjxYutyGY/9oAY0gkomiFNRNHjDwi4vH64vH70eP3w+gWS+Vdee7cHu+vagvOxj3fA6498LS7INoUWUCnKSmEDoBdDOgExpIkSgxACbl8Abm8APb3Bnay/Anu8fnx0rANVda3YXdcGu8sXsWyuWR+aj33hREtSd5MxpBMQQ5ooMfkDAg63Dw63D26vP97ViRt/QODgKRsqa4Ot7FOdrohlTTo1FhcF52NfPCU96fqxGdIJiCFNlPi8/gCcbj+cXh9c3kDStrCFEDjW5kRVXSuq6tpwuKkrYlmNSsK8/DSUl2SivNiKTJMuhjWND4Z0AmJIk1LEY8rZeJzmFggIuHzBwWd9A9GSdcGWNocbu48GNwL5Z8Pg/djTclJD87ELM8dnPzZDOgExpEkJqmpbsXlXHepaHPD6BTSqsV+2MB6vGQ9CBG+Ld7l8cCXxbfEejx8fHmtHZV0b9hxtQ9cg/dgTLMF+7IqSTMyZaIEqwT+49WFIJyCGNI3WaFujVbWtuGvbATjcPqQbtdCqZHj8AXT0bgDwwHVzoh6a8XhNJfD4AnB6fOjpneaVrC1sf0DgwEkbKmuDt8WbbJH7sc16NRYVBVvYC6akw6hN3H5shnQCYkjTaIy2NRoICHz72b043GRHrlkfdotRCIFmuxszJqTi+RsXRu02dDxeU4n6Ros73D50u31Ju5hKXz92ZW1wAZXPms/Tjz05HRW987GtCdaPzZBOQAxpGqlotEYPnLDh+y98hBSdGnqNqt/3e7x+ON0+bFmzAHMmWaJS73i8ptIJIdDt8cPe403qW+IAcKYr2I9dVduK6sbOQfuxp+emoqLEivLiTEyxGhXfjx3rkE7cew5ECS4QENi8qw4Oty+sNaqXVcg1y2i2u7F5Vx0WF1kHbY22Oz3w+gW0Eeau6lQybAGBdqcnanWPx2sqnSRJMOnUMOnUcHp8aO/2DLrpxXiWlarDV+fm4atz8+D0+LC3Pjgfe8/Rdjjc4f3YnzZ34dPmLjz9/jHkpelRUZyJ8hIrZueNn37s0WBIE8XJwVN21LU4kG7U9ms9SJKENKMGdS0OHDxlH7Q1mmHUQqOS4PEHoJf7t2rdfj8ggPrW7qiNvj7/awagkSVkGLWjep1EZdSqYdSq4XD70NHtSerlSY1aNS6dloVLp2XB5w/g45M2VNW1oaq2Dc328H7sU50u/Pe+E/jvfSdg1qtRVhxsYS+Ykg7DAHdskgFDmihOotUanZVnRnG2CYebupBrlsMCv8vlxcnOHkiQ8Lu3P4dWLUdl9PVgrymEQKfTixkTUjErL7m7f/pa1naXF53d3qTfvUutkjF/cjrmT07Hv19ajKOt3ajqXUDlyOnwzYXsLh/+cfA0/nHwNDQqCaUF6SgvDs7HzkhJng9/7JOOAvZJ00hEs1/3i75tP9KMGuhUMjp7vGiyBXdAmmDRI82gjero64Fe0+0PoHOcj+4eKSEE7C4f7D3epG5ZR3Kmyx3cuau2DTWNnQPuTw4ENx6aMSEV5cWZqCixYnJGbPuxOXAsATGkaSS+GCHdhVyzbtQjpM8dJW53eREQAhPTDEjVa0Z17iG9ZkBAI4/PedLR5vT4YOvxoseT3APMInG4ffiwPjgf+4P6NnS7I1+nSemG4Hzs4kzMzDOPeT82QzoBMaRppKLdGu2bb/3P4x14/H8/R5pBA8MAc1KjOfp6PK44Fitunx8d3V44PZEXBUl2Xn8AH58I9mNX1raipcsdsazFoMHiouD+2KUF6QPeoRothnQCYkjTaIxFa3TXkTO446/7kZ2qGzAwAwGBFocbv/nGXCydmjXat0Cj5PL60eH0xL1lHRACtae7YXN5YNFrUZKTAllBU6KEEKhtcQQDu64NtS2OiGV1arm3H9uKsmIr0qM0iJFTsIiSTHlJJhYXWaPaGuXo68Si16gwwWKA2+eHvSe4MEqsVzKrbujAi3sb0djWHfqwmG9NweqF+Zg3OT2mdYlEkiRckJOKC3JS8e3yKThtd/WOFG9FzQlb2GIybl8g+L26NkgAZuaZgwuolGRicoYxfm9imNiSjgK2pElpot3fTbEVCAh0uX3ocnljMte6uqEDD+88AqfHD7NeA41KCo1rMGpVWH/5VMUEdSQOlw8f1Lejqq4Ve+vb0T3IXYn8vn7skkzMmDC8fmze7k5ADGlSIo6+Hh9cXj/sLi+63f4x2T4zIAR+svUAjp5xINOkhYSzPtBBoNXhQVGWCQ99fY6ibn0PxusPYH9jJyp752OfcUTux043arC4KLhE6YKCdOjO04/NkE5ADGlSKo6+Hj98vdPnulzeqJ73SLMDP3/tAAxaNXTq/nP2Xb4AXB4f7l8xB1NzTSN6jXj2dQsh8HmLIzQfu+5Md8SyOrWMBQXpKC/JRFlRBtIG6A5inzQRRc1Y9HdTfKhVMrJSdbAYNOhwetDtjs6IcJvLA29AwKwa+N+EViWhSwjYXCNb4nUofd1jGeKSJGFqTiqm5qTiOxVT0GxzBedj17Vhf2Mnzp6O7fYFUNk7KE2Wgov29M3HnpQen35stqSjgC1pIoo1jy8Au8sLh2t0g8zGsiU9lL5uAHEbsNbl8gb7sWvbsPdYO5yD9GMXZBhRXmLFkgsyceWsCTH7oMuQjgKGNBHFy2gHmY1Vn/RQzptl0sLh8aNHAQPWPL4A9p/oRGXvbfE2R+Q7B1mpOqxeOBk/6v2QMZZ4u5uIKIHJsgSLQQOLQQOX148u1/CmcMmShNUL8/HwziNodXiQqtdAq5Lg8Qt09Ybl6oX5w779XHu6G41t3TDrNWEBDQASgjuG1bc6odeokGvRhcro1BIyTVq0Ojx4cW8j5uanxaT/WquWcfGUDFw8JQO3faUER047UFnXiqraNhxtDe/HPtPl7reb11hhSBMRjRN6jQp6jQrpRg3auz1DDpJ5k9Ox/vKpodvOXUJAI0koyjKN+Lbz+fq6BQBfQMCgkQcM8VS9Bo1t3ag93T3iAWsjJUkSpuWmYlpuKv6tohCnOnt651y34sAJG/wCuHxmTkzqwpAmIhpn1CoZ2WY9Uj1+tDrcQ9rQY97kdMzNT4vaAC6LXguNHLx9rVP3P4fbG+z/1UaY8jTaAWvRlJdmwP8pnYT/UzoJ3W4f6s50Y0FBbG7DM6SJiMYpg1aFSekG2F0+2Jzn3ypTlqSotVpLclKQb02J2Cft8gagVkmINP7K4w+25i16Za2KZzZocO28iTF7vYE3siUionFBkoJ91vkZBmSm6qCWY/Nrv6+v26hVodXhgcsXQEAIuHyB3r5vFQqtRnS5fBAI7z8XCPaH51tTUJITuznJSsSQJiJKApIkwawPhrXVFJuw7uvrLsoyweXxoc3pgcvjQ1GWCbcvm4bvLy2OGOIjHbA23nAKVhRwChYRJRohBOw9PnT2eMI2phgLgy1WErbYSe+ANaVt7HE2LguagBjSNF5xr+jxLxAQaHd6YO+J7nKjw6qDwrfIPBuXBSUiRQhb99svoFFx3e/xSJYlZJp0SNWr0ebwwOWN/Z7W0RywNt6wT5qI+unbQetwkx0pOjWyU3VI0alxuKkLd207gKra1nhXkaJMp1YhL82ArBgOLqPz40+CiMIEAgKbd9XB4fYh16yHXqOCLEvBlaHMOjjcfmzeVYfAGPdjUnyk6jWYlG6AxaAJ24ec4oMhTURhDp6yo67FgXSjtt8vaUmSkGbUoK7FgYOn7HGqIY01WZZgNekwMc1w3v2VaWwxpIkoTLvTA69fQKsa+NeDTiXD2zvYiMY3rVrGxDQDMlL6f2Cj2GBIE1GYDKMWGpUET4SlJN3+ADSyhAyjslaCorGTZtSyVR0nDGkiCjMrz4zibBM6nF6cO0NTCIFOpxfF2SbMyuN0w2TS16q2pujYqo4hhjRRHAQCAgdO2LDryBkcOGFT1CAsWZZwy9JimHQqNNvd6PH6EQgI9Hj9aLa7YdKpcMvSYs6XTlIWowYT0wzQs1UdE1zMJAq4mAkNR6LMPw6rZ0BAIyuznhQ/dpcX7Q7PkPeuHg+44lgCYkjTUPXNP3a4fUg3aqFVyfD4A+hwemHSqfDAdXMUFYBccYzOx+cPoK3bg+4h7l2d6LjiGNE4de78475+Pb2sQq5ZRrPdjc276rC4yKqYIJRlCXMmWeJdDVIwtUpGjlkPp8eH9m4PPL7z711NQ8c+aaIY4fxjGs+MWjUmpRuRlaqDJsL0PRo+tqSJYmQo849tnH9MCS5Vr4FJp4bd5YPN6YUvwJb1aPDjDlGMcP4xJQtJkmAxBPeuzkjRKnZHq0TAkCaKEc4/jkzJU9Jo5ILdOFrkZxhh0vPG7UjwqhHFSN/847u2HUCz3Y00owY6lQy3P4DO3tHdyTj/OFGmpNHIqWQJ2al6pOr8aHW44Y1wN4n64xSsKOAULBoOzj/+QqJNSaPRE0KgvdsDW4833lUZEU7BIhrnyksysbjImvTzjxNxShqNniQFd9gyaFU40+WGn10bg2JIE8UB5x8Pb0pasl+r8cioVWNimowzDjd6PP54V0exOHCMiOKCW2KSWiVjgsWAzFQd1DLjaCC8KkQUF5ySRn3Meg0mpRuQbuR0rXMxpIkoLjgljc4myxLSU7SYlG5Aql4T7+ooBkOaiOKCW2LSQNQqGVmpOkxMN8Cg5XaYDGkiipvykkw8cN0czJiQCqfbhxaHG063DzMmpHL6VZLTqVWYYDEgx6yHKok/qCVMSHd0dGDNmjWwWCywWCxYs2YNOjs7B33Oq6++iiuuuAKZmZmQJAk1NTX9yrjdbvy///f/kJmZiZSUFHz1q1/FiRMnxuZNEFE/5SWZeP7GhdiyZgF+84252LJmAZ6/cSEDmgAAKTo1JqYZoNckZ6s6YUJ69erVqKmpwY4dO7Bjxw7U1NRgzZo1gz6nu7sbFRUVePDBByOWWbduHbZt24a//OUveP/99+FwOHDNNdfA7+eUAKJY6ZuStnRqFuZMsvAWN4VRq2TkpQUHliWbhFhx7PDhw5g5cyb27NmDRYsWAQD27NmDsrIyfPrpp5g2bdqgzz927BgKCwtRXV2Niy66KHTcZrMhKysLL7zwAlatWgUAOHXqFPLz87F9+3ZcccUVQ6ofVxwjIooNl9ePFrs7brtrxXrFsYRoSe/evRsWiyUU0ACwePFiWCwWVFVVjfi8+/btg9frxbJly0LH8vLyMHv27EHP63a7Ybfbwx5ERDT29BoVJqYbkKJLjrW4EiKkm5ubkZ2d3e94dnY2mpubR3VerVaL9PT0sOM5OTmDnnfjxo2hvnGLxYL8/PwR14GIiIZHJUvIMeuRmarrt1rdeBPXkL733nshSdKgj48++ggABvxBCCHG5Ad0vvPeeeedsNlsoUdjY2PU60BERIMz6zWYmGaAVp0Q7c0Riev9gh/+8Ie4/vrrBy0zZcoUfPzxxzh9+nS/7505cwY5OTkjfv3c3Fx4PB50dHSEtaZbWlpQXl4e8Xk6nQ46nW7Er0tERNGhVcuYmGZAW7cH9gTdWWswcQ3pzMxMZGaef5pFWVkZbDYb9u7di4ULFwIAPvjgA9hstkHD9HxKS0uh0Wiwc+dOrFy5EgDQ1NSETz75BL/+9a9HfF4iIoodSZKQadLBqFWhtcsTt0FlYyEh7hHMmDEDV155JW6++Wbs2bMHe/bswc0334xrrrkmbGT39OnTsW3bttDX7e3tqKmpwaFDhwAAn332GWpqakL9zRaLBTfddBNuv/12vP3226iursY3v/lNzJkzB5dddlls3yQREY2KUased8uKJkRIA8Cf//xnzJkzB8uWLcOyZctw4YUX4oUXXggr89lnn8Fms4W+fv311zFv3jxcffXVAIDrr78e8+bNw5NPPhkq88gjj+Daa6/FypUrUVFRAaPRiDfeeAMqVXJOnCciSmSyLCErVYcJFgM0EXZYSyQJMU9a6ThPmohIeYQQsLt86HR64A9EJ+piPU86OSaaERFR0pEkCRaDBqk6NTp7vOhMwL3JE/9eABER0SBkWUJGihZ5aYl3CzyxaktERDRCeo0KE9MSa2AZQ5qIiJJG38CyRNkCkyFNRERJJ0WnxqR0I4xaZQ/NYkgTEVFSUskSci3BNcBlha4BzpAmIqKkZtZrMDHdoMhWNUOaiIiSnkYlI9eiR1aqTlF91QxpIiKiXqm9O2sZtMpYdZIhTUREdBa1SsYEiwHpRm28q8KQJiIiGkh67wIoajl+UcmQJiIiikCvUWFiugEpuvgMKmNIExERDUIlS8gxx2eqlvLGmxMRESmQWa+BURPbAWVsSRMREQ2ROsYbdDCkiYiIFIohTUREpFAMaSIiIoViSBMRESkUQ5qIiEihGNJEREQKxZAmIiJSKIY0ERGRQjGkiYiIFIohTUREpFAMaSIiIoViSBMRESkUQ5qIiEihGNJEREQKxZAmIiJSKIY0ERGRQjGkiYiIFEod7wqMB0IIAIDdbo9zTYiIKJGkpqZCkqSI32dIR0FXVxcAID8/P841ISKiRGKz2WA2myN+XxJ9zUAasUAggFOnTp33E1Gis9vtyM/PR2Nj46D/qCgyXsPR4zUcPV7D0YvWNWRLOgZkWcakSZPiXY2YMZvN/I89SryGo8drOHq8hqM31teQA8eIiIgUiiFNRESkUAxpGjKdTod77rkHOp0u3lVJWLyGo8drOHq8hqMXq2vIgWNEREQKxZY0ERGRQjGkiYiIFIohTUREpFAMaSIiIoViSCe5TZs2obCwEHq9HqWlpXjvvfcGLb9r1y6UlpZCr9ejqKgITz75ZNj3vV4v7r//fhQXF0Ov12Pu3LnYsWPHWL6FuBvONWxqasLq1asxbdo0yLKMdevWDVhu69atmDlzJnQ6HWbOnIlt27aNUe3jL9rX7+DBg/j617+OKVOmQJIkPProo2NXeYWI9jV86qmnsGTJEqSnpyM9PR2XXXYZ9u7dO4bvIP6ifQ1fffVVLFiwAGlpaUhJScFFF12EF154Ydj1YkgnsZdffhnr1q3Dhg0bUF1djSVLlmD58uVoaGgYsHx9fT2uuuoqLFmyBNXV1bjrrrtw6623YuvWraEyd999N7Zs2YLHH38chw4dwtq1a3Hdddehuro6Vm8rpoZ7Dd1uN7KysrBhwwbMnTt3wDK7d+/GqlWrsGbNGuzfvx9r1qzBypUr8cEHH4zlW4mLsbh+TqcTRUVFePDBB5GbmzuW1VeEsbiG77zzDv7v//2/+N///V/s3r0bkydPxrJly3Dy5MmxfCtxMxbXMCMjAxs2bMDu3bvx8ccf48Ybb8SNN96If/zjH8OrnKCktXDhQrF27dqwY9OnTxc//elPByz/4x//WEyfPj3s2Pe//32xePHi0NcTJkwQv/vd78LKrFixQtxwww1RqrWyDPcanm3p0qXitttu63d85cqV4sorrww7dsUVV4jrr79+VHVVorG4fmcrKCgQjzzyyChqqHxjfQ2FEMLn84nU1FTx/PPPj7SaihaLayiEEPPmzRN33333sOrGlnSS8ng82LdvH5YtWxZ2fNmyZaiqqhrwObt37+5X/oorrsBHH30Er9cLIPgJU6/Xh5UxGAx4//33o1h7ZRjJNRyKSNd5NOdUorG6fskkVtfQ6XTC6/UiIyMjaudUilhcQyEE3n77bXz22We45JJLhvVchnSSam1thd/vR05OTtjxnJwcNDc3D/ic5ubmAcv7fD60trYCCIbJww8/jM8//xyBQAA7d+7Ea6+9hqamprF5I3E0kms4FJGu82jOqURjdf2SSayu4U9/+lNMnDgRl112WdTOqRRjeQ1tNhtMJhO0Wi2uvvpqPP7447j88suHdQ7ugpXkzt0iTQgx6LZpA5U/+/hvf/tb3HzzzZg+fTokSUJxcTFuvPFGPPvss1GuuXIM9xrG65xKlUzvdayM5TX89a9/jZdeegnvvPNOv7tk48lYXMPU1FTU1NTA4XDg7bffxvr161FUVIRLL710yOdgSCepzMxMqFSqfp8UW1pa+n2i7JObmztgebVaDavVCgDIysrC//zP/8DlcqGtrQ15eXn46U9/isLCwrF5I3E0kms4FJGu82jOqURjdf2SyVhfw9/85jd44IEH8NZbb+HCCy8c9fmUaCyvoSzLKCkpAQBcdNFFOHz4MDZu3DiskObt7iSl1WpRWlqKnTt3hh3fuXMnysvLB3xOWVlZv/JvvvkmFixYAI1GE3Zcr9dj4sSJ8Pl82Lp1K1asWBHdN6AAI7mGQxHpOo/mnEo0VtcvmYzlNfzP//xP/OIXv8COHTuwYMGCUZ1LyWL571AIAbfbPewnUZL6y1/+IjQajXj66afFoUOHxLp160RKSoo4duyYEEKIn/70p2LNmjWh8kePHhVGo1H86Ec/EocOHRJPP/200Gg04pVXXgmV2bNnj9i6dauoq6sT7777rvjyl78sCgsLRUdHR6zfXkwM9xoKIUR1dbWorq4WpaWlYvXq1aK6ulocPHgw9P3KykqhUqnEgw8+KA4fPiwefPBBoVarxZ49e2L63mJhLK6f2+0OlZkwYYK44447RHV1tfj8889j+t5iZSyu4UMPPSS0Wq145ZVXRFNTU+jR1dUV0/cWK2NxDR944AHx5ptvirq6OnH48GHxX//1X0KtVounnnpqWHVjSCe5J554QhQUFAitVivmz58vdu3aFfret7/9bbF06dKw8u+8846YN2+e0Gq1YsqUKWLz5s39vj9jxgyh0+mE1WoVa9asESdPnozFW4mb4V5DAP0eBQUFYWX++7//W0ybNk1oNBoxffp0sXXr1hi8k/iI9vWrr68fsMy55xlPon0NCwoKBixzzz33xOYNxUG0r+GGDRtESUmJ0Ov1Ij09XZSVlYm//OUvw64Xt6okIiJSKPZJExERKRRDmoiISKEY0kRERArFkCYiIlIohjQREZFCMaSJiIgUiiFNRESkUAxpIiIihWJIE1HU3HvvvbjoootCX3/nO9/BtddeG/N6HDt2DJIkoaamJuavTRRNDGmiJPCd73wHkiRBkiRoNBoUFRXhjjvuQHd395i+7m9/+1s899xzQyrLYCXqj1tVEiWJK6+8Es8++yy8Xi/ee+89fPe730V3dzc2b94cVs7r9fbb1WykLBZLVM5DlKzYkiZKEjqdDrm5ucjPz8fq1atxww034H/+539Ct6ifeeYZFBUVQafTQQgBm82G733ve8jOzobZbMaXv/xl7N+/P+ycDz74IHJycpCamoqbbroJLpcr7Pvn3u4OBAJ46KGHUFJSAp1Oh8mTJ+NXv/oVAIT2HJ83bx4kSQrbc/fZZ5/FjBkzoNfrMX36dGzatCnsdfbu3Yt58+ZBr9djwYIFqK6ujuKVI4oftqSJkpTBYIDX6wUA1NbW4q9//Su2bt0KlUoFALj66quRkZGB7du3w2KxYMuWLfjKV76CI0eOICMjA3/9619xzz334IknnsCSJUvwwgsv4LHHHkNRUVHE17zzzjvx1FNP4ZFHHsGXvvQlNDU14dNPPwUQDNqFCxfirbfewqxZs6DVagEATz31FO655x787ne/w7x581BdXY2bb74ZKSkp+Pa3v43u7m5cc801+PKXv4w//elPqK+vx2233TbGV48oRka0pxcRJZRvf/vbYsWKFaGvP/jgA2G1WsXKlSvFPffcIzQajWhpaQl9/+233xZms1m4XK6w8xQXF4stW7YIIYQoKysTa9euDfv+okWLxNy5cwd8XbvdLnQ6XcT9dPu2mKyurg47np+fL1588cWwY7/4xS9EWVmZEEKILVu2iIyMDNHd3R36/ubNmwc8F1Gi4e1uoiTxt7/9DSaTCXq9HmVlZbjkkkvw+OOPAwAKCgqQlZUVKrtv3z44HA5YrVaYTKbQo76+HnV1dQCAw4cPo6ysLOw1zv36bIcPH4bb7cZXvvKVIdf5zJkzaGxsxE033RRWj1/+8pdh9Zg7dy6MRuOQ6kGUSHi7myhJ/Mu//As2b94MjUaDvLy8sMFhKSkpYWUDgQAmTJiAd955p9950tLSRvT6BoNh2M8JBAIAgre8Fy1aFPa9vtvyQogR1YcoETCkiZJESkoKSkpKhlR2/vz5aG5uhlqtxpQpUwYsM2PGDOzZswff+ta3Qsf27NkT8ZwXXHABDAYD3n77bXz3u9/t9/2+Pmi/3x86lpOTg4kTJ+Lo0aO44YYbBjzvzJkz8cILL6Cnpyf0QWCwehAlEt7uJqJ+LrvsMpSVleHaa6/FP/7xDxw7dgxVVVW4++678dFHHwEAbrvtNjzzzDN45plncOTIEdxzzz04ePBgxHPq9Xr85Cc/wY9//GP88Y9/RF1dHfbs2YOnn34aAJCdnQ2DwYAdO3bg9OnTsNlsAIILpGzcuBG//e1vceTIERw4cADPPvssHn74YQDA6tWrIcsybrrpJhw6dAjbt2/Hb37zmzG+QkSxwZAmon4kScL27dtxySWX4N/+7d8wdepUXH/99Th27BhycnIAAKtWrcLPf/5z/OQnP0FpaSmOHz+OW265ZdDz/uxnP8Ptt9+On//855gxYwZWrVqFlpYWAIBarcZjjz2GLVu2IC8vDytWrAAAfPe738Uf/vAHPPfcc5gzZw6WLl2K5557LjRly2Qy4Y033sChQ4cwb948bNiwAQ899NAYXh2i2JEEO3SIiIgUiS1pIiIihWJIExERKRRDmoiISKEY0kRERArFkCYiIlIohjQREZFCMaSJiIgUiiFNRESkUAxpIiIihWJIExERKRRDmoiISKH+P2apr9+wQbb3AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
+    "# Búsqueda bayesiana de hiperparámetros con optuna\n",
+    "# ==============================================================================\n",
+    "def objective(trial):\n",
+    "    params = {\n",
+    "        'n_estimators': trial.suggest_int('n_estimators', 10, 10000, step=10),\n",
+    "        'max_depth': trial.suggest_int('max_depth', 3, 300),\n",
+    "        'reg_lambda': trial.suggest_float('reg_lambda', 1e-5, 1e+10, log=True),\n",
+    "        'reg_alpha': trial.suggest_float('reg_alpha', 1e-5, 1e+10, log=True),\n",
+    "        'gamma': trial.suggest_float('gamma', 1e-5, 1e+10, log=True),\n",
+    "        'subsample': trial.suggest_float('subsample', 0.1, 1),\n",
+    "        'colsample_bynode': trial.suggest_float('colsample_bynode', 0.1, 1),\n",
+    "    }\n",
     "\n",
+    "    model = XGBRFRegressor(\n",
+    "                    tree_method        = 'hist',\n",
+    "                    grow_policy        = 'depthwise',\n",
+    "                    learning_rate      = 1.0,\n",
+    "                    n_jobs             = -1,\n",
+    "                    random_state       = 42,\n",
+    "                    enable_categorical = True,\n",
+    "                    **params\n",
+    "            )\n",
+    "    model.fit(X_train, y_train)\n",
+    "    predictions = model.predict(X_val)\n",
+    "    score = mean_squared_error(y_val, predictions, squared=False)\n",
+    "    return score\n",
+    "\n",
+    "study = optuna.create_study()#direction='minimize')\n",
+    "study.optimize(objective, n_trials=100, show_progress_bar=True, timeout=60*10)\n",
+    "\n",
+    "print('Mejores hiperparámetros:', study.best_params)\n",
+    "print('Mejor score:', study.best_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5a2fb58-9fd8-498f-942a-f81625695578",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Random Forest XGBoost con los mejores hiperparámetros encontrados\n",
+    "# ==============================================================================\n",
+    "model = XGBRFRegressor(\n",
+    "                tree_method        = 'hist',\n",
+    "                grow_policy        = 'depthwise',\n",
+    "                learning_rate      = 1.0,\n",
+    "                n_jobs             = -1,\n",
+    "                random_state       = 42,\n",
+    "                enable_categorical = True,\n",
+    "                missing = 0,\n",
+    "                n_estimators = 1630, \n",
+    "                max_depth= 152, \n",
+    "                reg_lambda= 2.527397980575116e-05, \n",
+    "                reg_alpha= 4.030923440517432e-05, \n",
+    "                gamma= 9.508723888858881e-05, \n",
+    "                subsample= 0.44422331389412245, \n",
+    "                colsample_bynode= 0.4919001135458046\n",
+    "             )\n",
+    "\n",
+    "# Entrenamiento del modelo\n",
+    "start = time.time()\n",
+    "model.fit(\n",
+    "        X = pd.concat([X_train, X_val]),\n",
+    "        y = pd.concat([y_train, y_val])\n",
+    ")\n",
+    "end = time.time()\n",
+    "tiempo_entrenamiento_xgboost = end - start\n",
+    "\n",
+    "# Predicciones test\n",
+    "start = time.time()\n",
+    "predicciones = model.predict(X=X_test)\n",
+    "end = time.time()\n",
+    "tiempo_prediccion_xgboost = end - start\n",
+    "\n",
+    "# Error de test del modelo\n",
+    "rmse_rf_xgboost = mean_squared_error(\n",
+    "                      y_true  = y_test,\n",
+    "                      y_pred  = predicciones,\n",
+    "                      squared = False\n",
+    "                  )\n",
+    "                 \n",
+    "print(f\"Tiempo entrenamiento: {tiempo_entrenamiento_xgboost:.2f} segundos\")\n",
+    "print(f\"Tiempo predicción:    {tiempo_prediccion_xgboost:.2f} segundos\")\n",
+    "print(f\"RMSE:                 {rmse_rf_xgboost:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d97affd-fdbc-426b-be71-f4ef0665610e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = []\n",
+    "mean_scores = []\n",
+    "metabs = pd.Series(y_test.columns)\n",
+    "modelname = \"pruebaRF\"\n",
+    "cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=42)\n",
+    "\n",
+    "for n in metabs:\n",
+    "    \n",
+    "    print(\" ----------------- \" + n + \" MODEL EVALUATION ----------------- \")\n",
+    "\n",
+    "    n_scores = cross_val_score(model, X_test, y_test[n], scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n",
+    "    n_scores = np.absolute(n_scores)\n",
+    "    scores.append(n_scores) \n",
+    "    mean_scores.append(np.mean(n_scores))\n",
+    "\n",
+    "    print('Full model ' + modelname + ' for metabolite '+n+ ' MAE: %.3f (%.3f)' % (np.mean(n_scores), np.std(n_scores)))\n",
+    "\n",
+    "# full model evaluation\n",
+    "print(\" ----------------- MEAN PERFORMANCE EVALUATION ----------------- \")\n",
+    "\n",
+    "n_score_all = cross_val_score(model, X_test, y_test, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)\n",
+    "n_score_all = np.absolute(n_score_all)\n",
+    "scores.append((n_score_all))\n",
+    "mean_scores.append(np.mean(n_score_all))\n",
+    "df_scores = pd.concat([metabs, pd.Series(scores), pd.Series(mean_scores)], axis = 1, ignore_index = True)\n",
+    "df_scores.columns = [\"Metabolite modeled\", \"MAE score\", \"Mean MAE score\"]\n",
+    "df_scores.to_csv(\"../results/predicts/scores_\" + modelname +\".csv\", sep = \",\")\n",
+    "\n",
+    "print(\"Mean performance of all model\" + modelname +  \" MAE: %.3f (%.3f)\" % (np.mean(n_score_all), np.std(n_score_all)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "949169e3-2419-499b-b068-f798d06a8f7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plotting\n",
+    "\n",
+    "print(\" ----------------- PLOTTING STARTING ----------------- \")\n",
+    "\n",
+    "# inherit model, modelname, df, df_name, testSplits\n",
+    "print(\" ----------------- PREDICTING AND JOINING PRED + TEST ----------------- \")\n",
+    "\n",
+    "# make predict\n",
+    "y_pred = model.predict(X_test)\n",
+    "# pred_prueba_DF = pd.DataFrame(pred_prueba, index = X_test.index-1, columns=y_test.columns).add_suffix(\"_pred\").join(y_test.add_suffix('_test')).fillna(0) \n",
+    "df_predTest = pd.DataFrame([i for i in y_pred], index = y_test.index, columns= y_test.columns).add_suffix(\"_pred\").join(y_test.add_suffix('_test')).fillna(0)\n",
+    "df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv(\"../results/predicts/df_pred-test_\"+modelname+\".csv\", sep = \",\")\n",
+    "    \n",
+    "# print plots\n",
+    "metabs = y_test.columns.drop(list(y_test.filter(regex='Sex|Sweetener'))) \n",
+    "\n",
+    "print(\" ----------------- STARTING PLOTTING ----------------- \")\n",
     "\n",
     "\n",
     "for metab in metabs:\n",
-    "    plt.figure()\n",
-    "\n",
     "    #sns.regplot(data = pred_prueba_DF, x=metab+\"_pred\", y=metab+\"_test\", )\n",
-    "    #ax = plt.gca()\n",
-    "    '''\n",
-    "    minPred = min(df_predTest[metab+\"_pred\"])\n",
-    "    maxPred = max(df_predTest[metab+\"_pred\"])\n",
-    "    minTest = min(df_predTest[metab+\"_test\"])\n",
-    "    maxTest = max(df_predTest[metab+\"_test\"])\n",
-    "    ax.set_xlim([min(minPred,  minTest),  max(maxPred,  maxTest)])\n",
-    "    ax.set_ylim([min(minPred,  minTest),  max(maxPred,  maxTest)])\n",
-    "    '''\n",
-    "    y_pred_metab = y_pred[metab]\n",
-    "    residuals = y_test[metab]-y_pred[metab]\n",
+    "    \n",
+    "    print(\" ----------------- PREDICTED VS TEST \" + metab + \" ----------------- \")\n",
+    "\n",
+    "    \n",
+    "\n",
+    "    adjustAxis = True\n",
+    "    if (adjustAxis):\n",
+    "        ax = plt.gca()\n",
+    "        minPred = min(df_predTest[metab+\"_pred\"])\n",
+    "        maxPred = max(df_predTest[metab+\"_pred\"])\n",
+    "        minTest = min(df_predTest[metab+\"_test\"])\n",
+    "        maxTest = max(df_predTest[metab+\"_test\"])\n",
+    "        ax.set_xlim([min(minPred,  minTest),  max(maxPred,  maxTest)])\n",
+    "        ax.set_ylim([min(minPred,  minTest),  max(maxPred,  maxTest)])\n",
+    "\n",
+    "    \n",
+    "    plt.figure(figsize = (10,10))\n",
+    "    sns.lmplot(data = df_predTest, x=metab+\"_pred\", y=metab+\"_test\")\n",
+    "    plt.title(\"TEST VS PREDICTED lmplot \" + metab + \" \" + modelname)\n",
+    "    plt.savefig(\"../results/plots/testVsPredicted_lmplot_\"+ metab+\"_\"+modelname+\".png\")\n",
+    "    plt.close(\"all\")   \n",
+    "\n",
+    "    print(\" ----------------- PREDICTED VS RESIDUALS \" + metab + \" ----------------- \")\n",
+    "\n",
+    "    plt.figure(figsize = (10,10))\n",
+    "    y_pred_metab = df_predTest[metab+\"_pred\"]\n",
+    "    residuals = df_predTest[metab+\"_test\"]-df_predTest[metab+\"_pred\"]\n",
     "    df_all = pd.concat([y_pred_metab, residuals], axis=1)\n",
     "    df_all.columns = [\"Predicted\", \"Residuals\"]\n",
     "    \n",
-    "    sns.lmplot(data = df_all, x=\"Predicted\", y=\"Residuals\")\n",
-    "    #plt.title(\"lmplot \" + metab + \" \" + modelname)\n",
-    "    #plt.savefig(\"lmplot_\"+ metab+\"_\"+modelname+\".png\")\n",
-    "    #sns.lmplot(data = df_all, x = y_pred[metab], y = y_test_metab-y_pred_metab)\n",
+    "    sns.scatterplot(data = df_all, x=\"Predicted\", y=\"Residuals\")\n",
+    "    plt.title(\"RESIDUALS VS PREDICTED \" + metab + \" \" + modelname)\n",
     "    plt.axhline(y=0)\n",
-    "    #plt.close()   \n"
+    "    plt.savefig(\"../results/plots/residualsVsPredicted_\"+ metab+\"_\"+modelname+\".png\")\n",
+    "    plt.close(\"all\")   "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4b9551c-5e38-4d2c-8543-aca42b650e40",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -743,7 +385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,
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diff --git a/scripts/testVsPredicted_lmplot_Weight_plasmAnt.png b/scripts/testVsPredicted_lmplot_Weight_plasmAnt.png
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diff --git a/scripts/utils.py b/scripts/utils.py
index d1539f6..7d11ab5 100644
--- a/scripts/utils.py
+++ b/scripts/utils.py
@@ -1,12 +1,13 @@
 import pandas as pd
 import re 
 import os
+import sys
 
 def fullRead(pathToTable, sep, full = False):
 
   df_renamed = pd.read_csv(pathToTable, sep = sep, encoding = "latin_1")
     
-  df_name = re.sub("_ord.csv","",(re.sub("data/", "" ,pathToTable)))
+  df_name = re.sub("_ord.csv","",(re.sub("../data/", "" ,pathToTable)))
   
 
   print ("Reading dataset " + df_name)
@@ -59,15 +60,16 @@ def fullRead(pathToTable, sep, full = False):
         
     df_renamed.drop(columns = ["Peso inicial", "Peso final", "Delta Peso", "Talla", "IMC Inicial", "IMC Final", "Delta IMC", "Grasa inicial", "Grasa final", "Delta Grasa", "IRCV Final", "IRCV inicial", "Bpmin final", "Bpmin inicial", "Bpmax final", "Bpmax inicial", "Frec final", "Frec inicial",], inplace=True )
   
-  #   
+    
   df_renamed.drop(columns = ["Unnamed: 0", "grouping"], inplace=True )
   
   print ("Filling NA with zeros")
   print ("#")
   print ("#")
   print ("#")  
-  df_renamed.fillna(0, inplace=True)
   
+  #df_renamed.fillna(0, inplace=True)
+    
   return (df_renamed, df_name)
 
 
diff --git a/untitled.txt b/untitled.txt
new file mode 100644
index 0000000..3f70a72
--- /dev/null
+++ b/untitled.txt
@@ -0,0 +1,8 @@
+Problema: desbalanceo de resultados debido a muchos ceros en algunas variables
+
+1. Probamos sklearn.RF con dataset urineFlav con dropna, que es el que menos afectado se ve
+
+2. Probamos XGBRF para manejar NAs sin necesidad de imputarlos a cero. 
+
+Posibles soluciones: 
+Hay que ver si hay implementación de MLP que maneje NAs
\ No newline at end of file