Implementation of DBSCAN model #75
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tests/test_dbscan.py
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| iris_target = iris.target | ||
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| def check_model_exist(path): |
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It seems that this function is not used?
| @@ -6,7 +6,7 @@ setup: ## Setup virtual environment for local development | |||
| && $(MAKE) install-requirements | |||
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| install-requirements: | |||
| pip install -U -e . | |||
| pip install --default-timeout=1000 -U -e . | |||
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Why increasing the timeout threshold ?
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Timeout occurred while installing the packages because of network instability.
| import pandas as pd | ||
| from sklearn import datasets, metrics | ||
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| def optimizer(): |
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It seems that it's not a NN model, should we remove this function?
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Yes, there is no need to set optimizer.
sqlflow_models/dbscan.py
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| return self | ||
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| def _split_dataset(self, dataset): |
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Do we actually need this function?
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Replace this function with _read_Dataset_data, which split tf.dataset type data into features and labels(If it exists).
sqlflow_models/dbscan.py
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| cluster_labels[self.clusters[i][j]] = i | ||
| return cluster_labels | ||
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| def fit(self, X): |
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Instead of overriding fit function, we can implement sqlflow_train_loop to define the custom train loop logic. You may find that sqlflow_train_loop is not the standard API of Keras model, it's just let SQLFlow runtime to know to run the custom train loop.
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Enables sqlflow_train_loop function to handle tf.dataset type of data, because sqlflow server invokes tf.dataset.
https://github.com/sql-machine-learning/sqlflow/blob/483b8676cf93f373d5073d84b0bee311bb122012/python/runtime/tensorflow/input_fn.py#L72
sqlflow_models/dbscan.py
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| purity_score(y_df, self.labels_)) | ||
| ''' | ||
| if __name__ == '__main__': | ||
| from sklearn.datasets.samples_generator import make_blobs |
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Can we remove the comment code and testing this model in the test_db_scan.py? A referenced test script: https://github.com/sql-machine-learning/models/blob/develop/tests/test_arima_with_stl_decomposition.py
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Comment code removed and rename the test_db_scan.py.
Implementation of the clustering model.
The test process will be submitted next time.