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| def make_prediction(X, clf_path, class_encoding_path, std_path, col_seq_path, good_features_path ): | |
| X = pd.DataFrame(X.values.reshape(1, -1), columns = X.index, dtype = float) | |
| kingdom_classes = ['Archea', 'Bacteria', 'Eukaryots'] | |
| #load class encoding | |
| with open(class_encoding_path, 'rb') as file: | |
| le = pickle.load(file) | |
| file.close() | |
| # load std_path file | |
| with open(std_path, 'rb') as file: | |
| std_ = pickle.load(file) | |
| file.close() | |
| # load classifier | |
| with open(clf_path, 'rb') as file: | |
| clf = pickle.load(file) | |
| file.close() | |
| # load col_seq | |
| with open(col_seq_path, 'rb') as file: | |
| col_seq = pickle.load(file) | |
| file.close() | |
| with open(good_features_path, 'rb') as file: | |
| good_features = pickle.load(file) | |
| file.close() | |
| # function for calculating double feature | |
| def get_XX_feature(val, f): | |
| temp = 0 | |
| for col in val.columns: | |
| if col[0:2]==f or col[-1:-3]==f or (col[0]==f[0] and col[-1]==f[0]): | |
| try: | |
| temp = temp + float(val.iloc[0][col]) | |
| except: | |
| print(f'There has been a error while calculating {f}') | |
| return temp | |
| # function for calculating single occurance feature | |
| def get_X_feature(val, f): | |
| temp = 0 | |
| for col in val.columns: | |
| if f in col and len(col)==3: | |
| try: | |
| temp = temp + float(val.iloc[0][col]) | |
| except: | |
| print(f'There has been a error while calculating {f}') | |
| return temp | |
| X_copy = X.copy() | |
| engineered_cols = ['kurt', 'median', 'mode','var', 'max', 'min', 'q1', 'q2', 'q3', 'std', 'sum', 'UU', 'AA', 'CC', 'GG', 'sum_g', 'sum_a', 'sum_c', 'sum_u'] | |
| kurt = X.kurtosis(axis = 1).values[0] | |
| med = X.median(axis = 1).values[0] | |
| mode = X.mode(axis = 1).values[0][0] | |
| var = X.var(axis = 1).values[0] | |
| max_ = X.max(axis = 1).values[0] | |
| min_ = X.min(axis = 1).values[0] | |
| q1 = X.quantile(0.25, axis = 1).values[0] | |
| q2 = X.quantile(0.50, axis = 1).values[0] | |
| q3 = X.quantile(0.75, axis = 1).values[0] | |
| std = X.std(axis = 1).values[0] | |
| sum_ = X.sum(axis = 1).values[0] | |
| UU = get_XX_feature(X, 'UU') | |
| AA = get_XX_feature(X, 'AA') | |
| CC = get_XX_feature(X, 'CC') | |
| GG = get_XX_feature(X, 'GG') | |
| sum_g = get_X_feature(X, 'G') | |
| sum_a = get_X_feature(X, 'A') | |
| sum_c = get_X_feature(X, 'C') | |
| sum_u = get_X_feature(X, 'U') | |
| X['kurt'] = kurt | |
| X['median'] = med | |
| X['mode'] = mode | |
| X['var'] = var | |
| X['max'] = max_ | |
| X['min'] = min_ | |
| X['q1'] = q1 | |
| X['q2'] = q2 | |
| X['q3'] = q3 | |
| X['std'] = std | |
| X['sum'] = sum_ | |
| X['UU'] = UU | |
| X['AA'] = AA | |
| X['CC'] = CC | |
| X['GG'] = GG | |
| X['sum_g'] = sum_g | |
| X['sum_a'] =sum_a | |
| X['sum_c'] = sum_c | |
| X['sum_u'] = sum_u | |
| X = pd.DataFrame(std_.transform(X, ), columns = X.columns) | |
| X = X[good_features] | |
| pred = clf.predict(X) | |
| return pred, kingdom_classes[pred[0]], X.values.tolist()[0] |
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