add logs
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@ -100,13 +100,13 @@ def train():
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# pred = np.array(linear_model.predict(feature_test_sample))
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# flatten_pred = flatten(pred)
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#
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## with open(cwd + "/../result.txt", "w+") as resultFile:
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# with open(cwd + "/../result.txt", "w+") as resultFile:
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# resultFile.write("predictions: " + str(flatten_pred) + '\n')
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# resultFile.write("expected: " + str(labels_test_sample.to_numpy()))
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# mlflow.log_param('epochs', numberOfEpochParam)
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# mlflow.log_param('learning_rate', learning_rate)
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# mlflow.log_metric('final_loss', min(hist["val_loss"]))
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mlflow.log_param('epochs', numberOfEpochParam)
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mlflow.log_param('learning_rate', learning_rate)
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mlflow.log_metric('final_loss', min(hist["val_loss"]))
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signature = mlflow.models.signature.infer_signature(house_price_features, linear_model.predict(house_price_features))
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