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| import pandas as pd | |
| import argparse | |
| import pickle | |
| import yaml | |
| from sklearn.preprocessing import LabelEncoder | |
| from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder | |
| import numpy as np | |
| def featurise_features(featdf, params): | |
| # adjust features dataset for chosen encoding | |
| game = featdf.iloc[:, :1] | |
| rounddata = featdf.iloc[:, 1:7] | |
| bontiles = featdf.iloc[:, 7:17] | |
| playerdata = featdf.iloc[:, 17:18] | |
| colours = featdf.iloc[:, 18:25] | |
| mapdata = featdf.iloc[:, -1:] | |
| onehot_encoder = OneHotEncoder(sparse=False) | |
| ordinal_encoder = OrdinalEncoder() | |
| if params['prepare-step2']['round-features'] == 'ordinal': | |
| rounddatanp = ordinal_encoder.fit_transform(rounddata) | |
| rounddata = pd.DataFrame(data=rounddatanp, columns=rounddata.columns) | |
| else: # one-hot | |
| rounddatanp = onehot_encoder.fit_transform(rounddata) | |
| rounddata = pd.DataFrame(data=rounddatanp, columns=onehot_encoder.get_feature_names()) | |
| if params['prepare-step2']['playercount-features'] == 'ordinal': | |
| playerdatanp = ordinal_encoder.fit_transform(playerdata) | |
| playerdata = pd.DataFrame(data=playerdatanp, columns=playerdata.columns) | |
| else: # one-hot | |
| playerdatanp = onehot_encoder.fit_transform(playerdata) | |
| playerdata = pd.DataFrame(data=playerdatanp, columns=onehot_encoder.get_feature_names()) | |
| if params['prepare-step2']['map-features'] == 'ordinal': | |
| mapdatanp = ordinal_encoder.fit_transform(mapdata) | |
| mapdata = pd.DataFrame(data=mapdatanp, columns=mapdata.columns) | |
| else: # one-hot | |
| mapdatanp = onehot_encoder.fit_transform(mapdata) | |
| mapdata = pd.DataFrame(data=mapdatanp, columns=onehot_encoder.get_feature_names()) | |
| featdf = pd.concat([game, rounddata, bontiles, playerdata, colours, mapdata], axis=1) | |
| return featdf | |
| def main(params): | |
| vpdfdir = params['prepare']['vp-data-dir'] | |
| featdfdir = params['prepare']['feature-data-dir'] | |
| pickledir = params['prepare-step2']['pickle-dir'] | |
| vpdf = pd.read_csv(vpdfdir) | |
| featdf = pd.read_csv(featdfdir) | |
| vpdf = vpdf.sort_values('game') | |
| featdf = featdf.sort_values('game') | |
| featdf = featdf.drop(columns=['Unnamed: 0']) | |
| featdf = featurise_features(featdf, params) | |
| each_faction_dataset = dict() | |
| colnames = list(vpdf.columns) | |
| factions = [x for x in colnames if x != 'game' and x != 'Unnamed: 0'] | |
| for faction in factions: | |
| faction_dataset = {} | |
| vpdf = vpdf.sort_index() | |
| indexes = pd.isnull(vpdf[faction]) | |
| vpdata = pd.Series(index=vpdf['game'][~indexes], data=vpdf[faction][~indexes].values) | |
| featdf = featdf.sort_index() | |
| featdata = featdf[~indexes] | |
| featdata.index = featdata['game'] | |
| featdata = featdata.drop(columns=['game']) | |
| faction_dataset['vp'] = vpdata | |
| faction_dataset['features'] = featdata | |
| each_faction_dataset[faction] = faction_dataset | |
| with open(pickledir, 'wb') as pklfile: | |
| pickle.dump(each_faction_dataset, pklfile) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser(description='Input DVC params.') | |
| parser.add_argument('--params', type=str) | |
| args = parser.parse_args() | |
| paramsdir = args.params | |
| with open(paramsdir, 'r') as fd: | |
| params = yaml.safe_load(fd) | |
| main(params) | |