2020-01-05 05:02:15 +00:00
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import pandas as pd
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import data.maker
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2020-01-10 19:12:58 +00:00
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from data.params import SYS_ARGS
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import json
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from scipy.stats import wasserstein_distance as wd
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import risk
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import numpy as np
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if 'config' in SYS_ARGS :
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ARGS = json.loads(open(SYS_ARGS['config']).read())
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if 'generate' not in SYS_ARGS :
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data.maker.train(**ARGS)
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else:
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#
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#
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_df = data.maker.generate(**ARGS)
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odf = pd.read_csv (ARGS['data'])
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odf.columns = [name.lower() for name in odf.columns]
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2020-02-11 18:00:16 +00:00
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column = ARGS['column'] if isinstance(ARGS['column'],list) else [ARGS['column']]
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print(pd.merge(odf,_df, on='id'))
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# print (_df[column].risk.evaluate(flag='synth'))
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# print (odf[column].risk.evaluate(flag='original'))
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# _x = pd.get_dummies(_df[column]).values
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# y = pd.get_dummies(odf[column]).values
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# N = _df.shape[0]
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# print (np.mean([ wd(_x[i],y[i])for i in range(0,N)]))
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# print (wd(_x[0],y[0]) )
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2020-01-10 19:12:58 +00:00
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# column = SYS_ARGS['column']
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# odf = open(SYS_ARGS['data'])
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