cross_val changes #29
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lacava
requested changes
Jul 8, 2017
few/few.py
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| @@ -213,7 +215,7 @@ def fit(self, features, labels): | |||
| initial_estimator = copy.deepcopy(self.ml.fit(x_t,y_t)) | |||
| # self._best_estimator = copy.deepcopy(self.ml.fit(x_t,y_t)) | |||
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| self._best_score = self.ml.score(x_v,y_v) | |||
| self._best_score = np.amax(cross_val_score(self.ml,x_t,y_t,cv=3)) | |||
few/few.py
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| #tmp_score = self.ml.score(self.transform( | ||
| # x_v,self.pop.individuals)[:,self.valid_loc()], | ||
| # y_v) | ||
| tmp_score = np.amax(cross_val_score(self.ml,x_t,y_t,cv=3)) |
lacava
reviewed
Jul 8, 2017
few/few.py
Outdated
| @@ -213,7 +215,7 @@ def fit(self, features, labels): | |||
| initial_estimator = copy.deepcopy(self.ml.fit(x_t,y_t)) | |||
| # self._best_estimator = copy.deepcopy(self.ml.fit(x_t,y_t)) | |||
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| self._best_score = self.ml.score(x_v,y_v) | |||
| self._best_score = np.amax(cross_val_score(self.ml,x_t,y_t,cv=3)) | |||
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I don't think you need to have cv=3 in there, that is the default
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yeah, realized that later..will do
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self._training_features is used in population.py in line 235 which is called in few.py line 50 few.py line 50 population.py line 235 |
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ok, let's leave _training_features in for now then. |
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I am not sure which is the best approach, commenting out the previous code or removing it. Thus, commented as of now.
Also, kept self._training_features and self._training_labels assigned as these are being used in functions in other python files, which are being called here.