(self, method, props, **kwargs)
| 410 | return "(step %d of %d) Processing %s" % (step_idx + 1, len(self.steps), name) |
| 411 | |
| 412 | def _check_method_params(self, method, props, **kwargs): |
| 413 | if _routing_enabled(): |
| 414 | routed_params = process_routing(self, method, **props, **kwargs) |
| 415 | return routed_params |
| 416 | else: |
| 417 | fit_params_steps = Bunch( |
| 418 | **{ |
| 419 | name: Bunch(**{method: {} for method in METHODS}) |
| 420 | for name, step in self.steps |
| 421 | if step is not None |
| 422 | } |
| 423 | ) |
| 424 | for pname, pval in props.items(): |
| 425 | if "__" not in pname: |
| 426 | raise ValueError( |
| 427 | "Pipeline.fit does not accept the {} parameter. " |
| 428 | "You can pass parameters to specific steps of your " |
| 429 | "pipeline using the stepname__parameter format, e.g. " |
| 430 | "`Pipeline.fit(X, y, logisticregression__sample_weight" |
| 431 | "=sample_weight)`.".format(pname) |
| 432 | ) |
| 433 | step, param = pname.split("__", 1) |
| 434 | fit_params_steps[step]["fit"][param] = pval |
| 435 | # without metadata routing, fit_transform and fit_predict |
| 436 | # get all the same params and pass it to the last fit. |
| 437 | fit_params_steps[step]["fit_transform"][param] = pval |
| 438 | fit_params_steps[step]["fit_predict"][param] = pval |
| 439 | return fit_params_steps |
| 440 | |
| 441 | def _get_metadata_for_step(self, *, step_idx, step_params, all_params): |
| 442 | """Get params (metadata) for step `name`. |
no test coverage detected