add save, load model function

BaseML/Classification.py

@@ -8,6 +8,7 @@
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.svm import SVC
+import joblib
 
 class cls:
     def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, ):
@@ -75,3 +76,11 @@ def load_dataset(self, path, test_size=0.2, dataset=''):
         self.dataset_path = path
         self.test_size = test_size
         self.dataset=dataset
+
+    def save(self):
+        print("Saving model checkpoints...")
+        joblib.dump(self.model, '../checkpoint.pkl', compress=3)
+
+
+    def load(self, path):
+        joblib.load(path)

BaseML/GaussianNB.py

@@ -1,6 +1,7 @@
 import pandas as pd
 import numpy as np
 import os
+import joblib
 
 from sklearn.metrics import accuracy_score, mean_squared_error
 from sklearn.naive_bayes import GaussianNB as gauss
@@ -16,6 +17,7 @@ def __init__(self
         self.test_size = ' '
         self.test_set = ' '
 
+
     def train(self, seed=0, data_type='csv'):
         np.random.seed(seed)
         if data_type == 'csv':
@@ -30,6 +32,8 @@ def train(self, seed=0, data_type='csv'):
             'label': label[train_index:]
         }
         self.model.fit(train_data, train_label)
+        return self.model
+
 
     def inference(self, mode='cls'):
         pred = self.model.predict(self.test_set['data'])
@@ -40,6 +44,16 @@ def inference(self, mode='cls'):
             loss = mean_squared_error(self.test_set['label'], pred)
             print('Loss: {}'.format(loss))
 
+
     def load_dataset(self, path, test_size=0.2):
         self.dataset_path = path
         self.test_size = test_size
+
+
+    def save(self):
+        print("Saving model checkpoints...")
+        joblib.dump(self.model, '../checkpoint.pkl', compress=3)
+
+
+    def load(self, path):
+        joblib.load(path)

BaseML/Regression.py

@@ -6,6 +6,7 @@
 from sklearn.metrics import accuracy_score, mean_squared_error, r2_score
 from sklearn import linear_model
 from sklearn.decomposition import PCA as pca_reduction
+import joblib
 
 class reg:
     def __init__(self, algorithm='', n_components='mle'):
@@ -55,3 +56,12 @@ def load_dataset(self,path,test_size=0.2, dataset=''):
         self.dataset_path = path 
         self.test_size = test_size
         self.dataset = dataset
+
+
+    def save(self):
+        print("Saving model checkpoints...")
+        joblib.dump(self.model, '../checkpoint.pkl', compress=3)
+
+
+    def load(self, path):
+        joblib.load(path)