refactoring

Classifier/svm.py

+import os
 import sys
 
 import numpy as np
@@ -8,6 +9,8 @@ from sklearn.preprocessing import KBinsDiscretizer
 from sklearn.svm import LinearSVC
 
 sys.setrecursionlimit(100000000)
+abspath = os.path.dirname(os.path.abspath(__file__))
+os.chdir(abspath)
 
 
 class Svm:
@@ -51,12 +54,12 @@ class Svm:
 
     def evaluate_set(self, vcc_feature_vectors, unclassified_feature_vectors):
         """"""
-        '''
+        """
         feature_vectors = self.preprocess(vcc_feature_vectors[0][0])
         for feature_vector in vcc_feature_vectors[1:]+unclassified_feature_vectors:
             feature_vectors = vstack((feature_vectors, self.preprocess(feature_vector[0])))
         dump(feature_vectors, "Vectors/testing_set.joblib")
-        '''
+        """
         feature_vectors = load("Vectors/testing_set.joblib")
 
         confidences = self.model.decision_function(feature_vectors)
@@ -102,30 +105,29 @@ class Svm:
         open("x_own", "w+").write(str(rec))
         open("y_own", "w+").write(str(pre))
 
-    def train_model(self, vcc_feature_vectors_and_weigts, unclassified_feature_vectors, fs=303, c=1, weight=1):
-
+    def train_model(self, vcc_feature_vectors_and_weights, unclassified_feature_vectors, fs=303, c=1, weight=1):
         labels = []
         weights = []
-        '''
+        """
         vcc_feature_vectors = [x[0] for x in vcc_feature_vectors_and_weigts]
         fit_vectors = [x.tocsc()[0, :303] for x in vcc_feature_vectors+unclassified_feature_vectors]
         # preprocess fit
         self.preprocess_fit(fit_vectors)
         #dump(self.preprocessing, "Models/preprocessing.joblib")
-        '''
+        """
         # self.preprocessing = load("Models/preprocessing.joblib")
 
-        '''
+        """
         feature_vectors = self.preprocess(vcc_feature_vectors[0])
         for feature_vector in vcc_feature_vectors[1:]+unclassified_feature_vectors:
             feature_vectors = vstack((feature_vectors, self.preprocess(feature_vector)))
         #dump(feature_vectors, "feature_vectors.joblib")
-        '''
+        """
         feature_vectors = load("feature_vectors.joblib")
 
-        for i, vector in enumerate(vcc_feature_vectors_and_weigts):
+        for i, vector in enumerate(vcc_feature_vectors_and_weights):
             labels.append(1)
-            weights.append(vcc_feature_vectors_and_weigts[i][1] * weight)
+            weights.append(vcc_feature_vectors_and_weights[i][1] * weight)
         for i in range(len(unclassified_feature_vectors)):
             labels.append(0)
             weights.append(1)
@@ -134,7 +136,7 @@ class Svm:
         self.model = LinearSVC(C=c, max_iter=100000000)
 
         # feature selection using k best
-        '''
+        """
         print("feature selection...")
         self.kbest = SelectKBest(chi2, k=int(fs))
         feature_vectors_best = self.kbest.fit_transform(feature_vectors_scaled, labels)
@@ -145,14 +147,14 @@ class Svm:
         self.feature_select.fit(feature_vectors_scaled, labels)
         feature_selected_vectors = self.feature_select.transform(feature_vectors_scaled)
         print(feature_selected_vectors.shape)
-        '''
+        """
 
-        '''
+        """
         self.model.fit(feature_vectors, labels, weights)
         print("Score:", str(self.model.score(feature_vectors, labels)))
         print("Done")
         self.confidences = {}
-        '''
+        """
         k = 5
         scores = cross_val_score(self.model, feature_vectors, labels, cv=k)
         print(scores)