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| # Copyright (c) ONNX Project Contributors | |
| # SPDX-License-Identifier: Apache-2.0 | |
| import numpy as np | |
| from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl | |
| class Imputer(OpRunAiOnnxMl): | |
| def _run( # type: ignore | |
| self, | |
| x, | |
| imputed_value_floats=None, | |
| imputed_value_int64s=None, | |
| replaced_value_float=None, | |
| replaced_value_int64=None, | |
| ): | |
| if imputed_value_floats is not None and len(imputed_value_floats) > 0: | |
| values = imputed_value_floats | |
| replace = replaced_value_float | |
| elif imputed_value_int64s is not None and len(imputed_value_int64s) > 0: | |
| values = imputed_value_int64s | |
| replace = replaced_value_int64 | |
| else: | |
| raise ValueError("Missing are not defined.") | |
| if isinstance(values, list): | |
| values = np.array(values) | |
| if len(x.shape) != 2: | |
| raise TypeError(f"x must be a matrix but shape is {x.shape}") | |
| if values.shape[0] not in (x.shape[1], 1): | |
| raise TypeError( # pragma: no cover | |
| f"Dimension mismatch {values.shape[0]} != {x.shape[1]}" | |
| ) | |
| x = x.copy() | |
| if np.isnan(replace): | |
| for i in range(0, x.shape[1]): | |
| val = values[min(i, values.shape[0] - 1)] | |
| x[np.isnan(x[:, i]), i] = val | |
| else: | |
| for i in range(0, x.shape[1]): | |
| val = values[min(i, values.shape[0] - 1)] | |
| x[x[:, i] == replace, i] = val | |
| return (x,) | |