0.0.4

BaseML/GaussianNB.py

@@ -3,15 +3,15 @@
 import os
 
 from sklearn.metrics import accuracy_score, mean_squared_error
-from sklearn.naive_bayes import GaussianNB
+from sklearn.naive_bayes import GaussianNB as gauss
 
 
 class GaussianNB:
     def __init__(self
                  ):
         self.cwd = os.path.dirname(os.getcwd())  # 获取当前文件的绝对路径
         self.file_dirname = os.path.dirname(os.path.abspath(__file__))
-        self.model = GaussianNB()
+        self.model = gauss()
         self.dataset_path = ' '
         self.test_size = ' '
         self.test_set = ' '

BaseML/PCA.py

@@ -1,4 +1,4 @@
-from sklearn.decomposition import PCA
+from sklearn.decomposition import PCA as pca_reduction
 import os
 
 
@@ -10,17 +10,17 @@ def __init__(self,
         self.cwd = os.path.dirname(os.getcwd())
         # 获取当前文件的绝对路径
         self.file_dirname = os.path.dirname(os.path.abspath(__file__))
-        self.dataset = ''
-        self.x_train, self.x_test = 0, 0
-        self.model = PCA(n_components=n_components)
+        self.dataset = None
+        # self.x_train, self.x_test = 0, 0
+        self.model = pca_reduction(n_components=n_components)
 
     def train(self):
         self.model.fit(self.dataset)
         print(self.model.explained_variance_ratio_)
         # 返回所保留的n个成分各自的方差百分比,这里可以理解为单个变量方差贡献率。
 
     def inference(self, data):
-        self.model.fit_transform(data)
+        self.model.transform(data)
         print(self.model.n_features_)
         print(self.model.n_samples_)
 

BaseML/Perceptron.py

@@ -1,4 +1,4 @@
-from sklearn.linear_model import Perceptron
+from sklearn.linear_model import Perceptron as per
 import os
 
 
@@ -11,7 +11,7 @@ def __init__(self
         self.file_dirname = os.path.dirname(os.path.abspath(__file__))
         self.dataset = ''
         self.x_train, self.x_test = 0, 0
-        self.model = Perceptron()
+        self.model = per()
 
     def train(self):
         self.model.fit(self.dataset)

BaseML/__init__.py

@@ -0,0 +1,17 @@
+from .CART import CART
+from .KNNClassifier import KNN
+from .PCA import PCA
+from .Perceptron import Perceptron
+from .AdaBoost import AdaBoost
+from .GaussianNB import GaussianNB
+from .SVM import SVM
+
+
+__all__ = [
+    'CART',
+    'SVM',
+    'AdaBoost',
+    'GaussianNB',
+    'KNN',
+    'PCA',
+    'Perceptron']