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@ -578,18 +578,20 @@ class Predict(GNet):
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# if we are dealing with numeric values only we can perform a simple marginal sum against the indexes
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# The code below will insure we have some acceptable cardinal relationships between id and synthetic values
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#
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df = pd.DataFrame(np.round(f).astype(np.int32))
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df = pd.DataFrame(np.round(f)).astype(np.int32)
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p = 0 not in df.sum(axis=1).values
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x = df.sum(axis=1).values
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if np.divide( np.sum(x), x.size) > .9 or p and np.sum(x) == x.size and x.size == self.values.size:
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if np.divide( np.sum(x), x.size) > .9 or p and np.sum(x) == x.size :
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ratio.append(np.divide( np.sum(x), x.size))
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found.append(df)
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if i == CANDIDATE_COUNT:
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break
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else:
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continue
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# i = df.T.index.astype(np.int32) #-- These are numeric pseudonyms
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@ -597,9 +599,13 @@ class Predict(GNet):
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#
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# In case we are dealing with actual values like diagnosis codes we can perform
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#
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_index = [found.index(item) for item in found if item.shape[1] == len(self.values)]
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if not _index :
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INDEX = np.random.choice(np.arange(len(found)),1)[0]
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INDEX = ratio.index(np.max(ratio))
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else:
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INDEX = _index[0]
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INDEX = np.random.choice(np.arange(len(found)),1)[0]
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INDEX = ratio.index(np.max(ratio))
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df = found[INDEX]
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columns = self.ATTRIBUTES['synthetic'] if isinstance(self.ATTRIBUTES['synthetic'],list)else [self.ATTRIBUTES['synthetic']]
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