Update sklearn_transformers.py

my_custom_sklearn_transforms/__init__.py

@@ -0,0 +1,3 @@
+from .sklearn_transformers import DropColumns
+from .coding import Coding
+from .decoding import Decoding

my_custom_sklearn_transforms/coding.py

@@ -0,0 +1,26 @@
+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
+
+
+# All sklearn Transforms must have the `transform` and `fit` methods
+class Coding(BaseEstimator, TransformerMixin):
+    def __init__(self, p):
+        self.p = p
+
+    def fit(self):
+        return self.p
+
+    def transform(self):
+        self.p1 = np.array([])
+        for i in range(0,len(self.p)):
+            if self.p[i] == "EXCELENTE":
+                self.p1 = np.append(self.p1, 1)
+            if self.p[i] == "MUITO_BOM":
+                self.p1 = np.append(self.p1, 2)
+            if self.p[i] == "HUMANAS":
+                self.p1 = np.append(self.p1, 3)
+            if self.p[i] == "EXATAS":
+                self.p1 = np.append(self.p1, 4)
+            if self.p[i] == "DIFICULDADE":
+                self.p1 = np.append(self.p1, 5)
+        return np.ravel(self.p1)

my_custom_sklearn_transforms/decoding.py

@@ -0,0 +1,26 @@
+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
+
+
+# All sklearn Transforms must have the `transform` and `fit` methods
+class Decoding(BaseEstimator, TransformerMixin):
+    def __init__(self, p):
+        self.p = p
+
+    def fit(self):
+        return self.p
+
+    def transform(self):
+        self.p1 = np.array([])
+        for i in range(0,len(self.p)):
+            if self.p[i] <= 1.0:
+                self.p1 = np.append(self.p1, "EXCELENTE")
+            if self.p[i] == 2.0:
+                self.p1 = np.append(self.p1, "MUITO_BOM")
+            if self.p[i] == 3.0:
+                self.p1 = np.append(self.p1, "HUMANAS")
+            if self.p[i] == 4.0:
+                self.p1 = np.append(self.p1, "EXATAS")
+            if self.p[i] >= 5.0:
+                self.p1 = np.append(self.p1, "DIFICULDADE")
+        return self.p1

my_custom_sklearn_transforms/sklearn_transformers.py

@@ -1,3 +1,4 @@
+# All sklearn Transforms must have the `transform` and `fit` methods
 from sklearn.base import BaseEstimator, TransformerMixin
 
 
@@ -8,9 +9,16 @@ def __init__(self, columns):
 
     def fit(self, X, y=None):
         return self
-
+    
     def transform(self, X):
         # Primeiro realizamos a cópia do dataframe 'X' de entrada
         data = X.copy()
         # Retornamos um novo dataframe sem as colunas indesejadas
-        return data.drop(labels=self.columns, axis='columns')
+        data = data.drop(labels=self.columns, axis='columns')
+        data = data.dropna(axis=0, subset=["NOTA_GO"])
+        df = data[['NOTA_DE' , 'NOTA_EM' , 'NOTA_MF' , 'NOTA_GO']]
+        data['MEDIA'] = df.mean(axis=1)
+        data['SOMA'] = df.sum(axis=1)
+        df = data[['NOTA_DE' , 'NOTA_EM', 'NOTA_GO']]
+        data['MEDIA_HUMANAS'] = df.mean(axis=1)
+        return data