Add callbacks to model search #42
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kmax12
commented
Sep 3, 2019
| if self.verbose: | ||
| self._log_title("Beginning pipeline search") | ||
| self._log("Optimizing for %s. " % self.objective.name, new_line=False) | ||
| self._log_title("Beginning pipeline search") |
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this changed due to updates to _log
| @@ -47,6 +50,9 @@ def __init__(self, problem_type, tuner, cv, objective, max_pipelines, max_time, | |||
| self.default_objectives = default_objectives | |||
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| def _log(self, msg, color=None, new_line=True): | |||
| if not self.verbose: | |||
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handling verbose here to avoid any printing when calling log
| @@ -33,4 +33,6 @@ def objective_function(self, y_predicted, y_true): | |||
| profit = self.true_positives * true_positives | |||
| profit += self.false_positives * false_positives | |||
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| return profit | |||
| profit_per_lead = profit / len(y_true) | |||
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squeezing this in to the PR. just updating the metric to be normalized per transaction considered
jeremyliweishih
approved these changes
Sep 3, 2019
| @@ -17,6 +17,8 @@ def __init__(self, | |||
| cv=None, | |||
| tuner=None, | |||
| detect_label_leakage=True, | |||
| start_iteration_callback=None, | |||
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Are there any useful callback functions we could include as a default?
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added #44 to finish writing documentation |
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Fixes #20
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