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@@ -139,7 +139,7 @@ class ConvGAN2(GanBaseClass):
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## generate synth_num synthetic samples from each minority neighbourhood
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synth_set=[]
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for i in range(len(data_min)):
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- synth_set.extend(self._generate_data_for_min_point(self.nmb, i, synth_num))
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+ synth_set.extend(self._generate_data_for_min_point(i, synth_num))
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synth_set = np.array(synth_set[:numOfSamples]) ## extract the exact number of synthetic samples needed to exactly balance the two classes
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self.timing["create points"].stop()
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@@ -266,7 +266,7 @@ class ConvGAN2(GanBaseClass):
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return model
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# Create synthetic points
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- def _generate_data_for_min_point(self, nmb, index, synth_num):
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+ def _generate_data_for_min_point(self, index, synth_num):
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"""
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generate synth_num synthetic points for a particular minoity sample
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synth_num -> required number of data points that can be generated from a neighbourhood
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@@ -279,7 +279,7 @@ class ConvGAN2(GanBaseClass):
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runs = int(synth_num / self.neb) + 1
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synth_set = []
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for _run in range(runs):
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- batch = self._NMB_guided(nmb, index)
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+ batch = self._NMB_guided(index)
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self.timing["predict"].start()
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synth_batch = self.conv_sample_generator.predict(batch)
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self.timing["predict"].stop()
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@@ -304,7 +304,7 @@ class ConvGAN2(GanBaseClass):
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for step in range(self.neb_epochs * len(data_min)):
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## generate minority neighbourhood batch for every minority class sampls by index
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- min_batch = self._NMB_guided(nmb, min_idx)
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+ min_batch = self._NMB_guided(min_idx)
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min_idx = min_idx + 1
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## generate random proximal majority batch
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maj_batch = self._BMB(data_min, data_maj)
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@@ -376,7 +376,7 @@ class ConvGAN2(GanBaseClass):
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def _NMB_prepare(self, data_min):
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self.timing["NMB"].start()
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t = time.time()
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- neigh = NNSearch(self.neb)
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+ neigh = NNSearch(self.neb, timingDict=self.timing)
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neigh.fit(data_min)
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self.tNbhFit += (time.time() - t)
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self.nNbhFit += 1
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@@ -384,7 +384,7 @@ class ConvGAN2(GanBaseClass):
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return (data_min, neigh)
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- def _NMB_guided(self, nmb, index):
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+ def _NMB_guided(self, index):
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## generate a minority neighbourhood batch for a particular minority sample
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## we need this for minority data generation
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@@ -393,7 +393,7 @@ class ConvGAN2(GanBaseClass):
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## data_min -> minority class data
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## neb -> oversampling neighbourhood
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self.timing["NMB"].start()
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- (data_min, neigh) = nmb
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+ (data_min, neigh) = self.nmb
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t = time.time()
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nmbi = np.array([neigh.neighbourhoodOfItem(index)])
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