From 0efb5eb6a505847ac9d43eee8af7c0da69cfd069 Mon Sep 17 00:00:00 2001
From: MauronMP <pablo.mopi@gmail.com>
Date: Wed, 16 Nov 2022 14:30:46 +0100
Subject: [PATCH] =?UTF-8?q?=F0=9F=91=B7=20build(regression=20&&=20model.py?=
 =?UTF-8?q?):=20Delete,=20no=20son=20necesarios=20para=20#26?=
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---
 pmp_iv/enums/regression_algorithm.py       |  7 ----
 pmp_iv/forest_prediction/model_building.py | 47 ----------------------
 2 files changed, 54 deletions(-)
 delete mode 100644 pmp_iv/enums/regression_algorithm.py
 delete mode 100644 pmp_iv/forest_prediction/model_building.py

diff --git a/pmp_iv/enums/regression_algorithm.py b/pmp_iv/enums/regression_algorithm.py
deleted file mode 100644
index af08ddb..0000000
--- a/pmp_iv/enums/regression_algorithm.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from enum import Enum
- 
-class Regression_algorithm(Enum):
-    Lasso = 'Lasso'
-    Ridge = 'Ridge'
-    RandomForestRegressor = 'RandomForestRegressor'
-    KNeighborsRegressor = 'KNeighborsRegressor'
\ No newline at end of file
diff --git a/pmp_iv/forest_prediction/model_building.py b/pmp_iv/forest_prediction/model_building.py
deleted file mode 100644
index bd1a847..0000000
--- a/pmp_iv/forest_prediction/model_building.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from sklearn.preprocessing import StandardScaler
-from sklearn.metrics import mean_absolute_error, r2_score
-from sklearn.linear_model import Lasso
-from sklearn.linear_model import Ridge
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.neighbors import KNeighborsRegressor
-from pmp_iv.forest_prediction.eda import *
-from sklearn.model_selection import train_test_split
-from pmp_iv.enums.regression_algorithm import *
-
-class model_building():
-    
-    def __init__(self):
-        self.data_csv = EDA()
-        X = EDA().weather()
-        Y = EDA().by_property('area')
-        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X,Y,test_size=0.20,shuffle=True,random_state=1)
-        
-    def scaler_standard(self, X_train, X_test):
-        scaler = StandardScaler()
-        X_train_scaled = scaler.fit_transform(X_train)
-        X_test_scaled = scaler.transform(X_test)
-    
-        return X_train_scaled, X_test_scaled
-           
-    def regresion_todos(self):
-        X_train_scaled, X_test_scaled = self.scaler_standard(self.X_train, self.X_test)
-        rSquare_mae = []
-        
-        for i_regresion in (Lasso(), Ridge(), RandomForestRegressor(), KNeighborsRegressor()):
-            i_regresion.fit(X_train_scaled, self.y_train)
-            i_regresion_prediccion = i_regresion.predict(X_test_scaled)
-            mae = mean_absolute_error(self.y_test, i_regresion_prediccion)
-            r2 = r2_score(self.y_test, i_regresion_prediccion)
-            rSquare_mae.append([r2,mae])
-            
-        j = 0
-        for i in Regression_algorithm:
-            rSquare_mae[j].append(i.value)
-            j+=1
-            
-        return rSquare_mae
-    
-    def get_best_results(self):
-        listado_resultados = self.regresion_todos()
-        listado_resultados = sorted(listado_resultados, key = lambda x: (-x[0]))
-        return listado_resultados[0][2]
\ No newline at end of file