continuous functions
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13
pipeline.py
13
pipeline.py
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@ -142,10 +142,15 @@ class Components :
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#
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#
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# We need to make sure that continuous columns are removed
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# We need to make sure that continuous columns are removed
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if x_cols :
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if x_cols :
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_args['data'] = df[list(set(df.columns) - set(x_cols))]
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_args['data'] = _args['data'][list(set(df.columns) - set(x_cols))]
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if 'gpu' in args :
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if 'gpu' in args :
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_args['gpu'] = self.set_gpu(gpu=args['gpu'])
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_args['gpu'] = self.set_gpu(gpu=args['gpu'])
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data.maker.train(**_args)
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if df.shape[0] and df.shape[0] :
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#
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# We have a full blown matrix to be processed
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data.maker.train(**_args)
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else:
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print ("... skipping training !!")
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if 'autopilot' in ( list(args.keys())) :
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if 'autopilot' in ( list(args.keys())) :
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@ -216,7 +221,7 @@ class Components :
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_dc = pd.DataFrame()
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_dc = pd.DataFrame()
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# for mdf in df :
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# for mdf in df :
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args['data'] = df
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args['data'] = df.copy()
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#
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#
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# The columns that are continuous should also be skipped because they don't need to be synthesied (like-that)
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# The columns that are continuous should also be skipped because they don't need to be synthesied (like-that)
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if 'continuous' in args :
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if 'continuous' in args :
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@ -232,7 +237,7 @@ class Components :
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# @TODO: Abstract this !!
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# @TODO: Abstract this !!
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#
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#
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if x_cols :
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if x_cols :
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args['data'] = df[list(set(df.columns) - set(x_cols))]
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args['data'] = args['data'][list(set(df.columns) - set(x_cols))]
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args['candidates'] = 1 if 'candidates' not in args else int(args['candidates'])
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args['candidates'] = 1 if 'candidates' not in args else int(args['candidates'])
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if 'gpu' in args :
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if 'gpu' in args :
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