提交 abe68b15 编辑于 作者: Haowen Xu's avatar Haowen Xu
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README.rst

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@@ -64,44 +64,32 @@ To construct a Donut model: 
    with tf.variable_scope('model') as model_vs:

        model = Donut(

            h_for_p_x=Sequential([

                K.layers.Dense(100, W_regularizer=K.regularizers.l2(0.001),

                K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),

                               activation=tf.nn.relu),

                K.layers.Dense(100, W_regularizer=K.regularizers.l2(0.001),

                K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),

                               activation=tf.nn.relu),

            ]),

            h_for_q_z=Sequential([

                K.layers.Dense(100, W_regularizer=K.regularizers.l2(0.001),

                K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),

                               activation=tf.nn.relu),

                K.layers.Dense(100, W_regularizer=K.regularizers.l2(0.001),

                K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),

                               activation=tf.nn.relu),

            ]),

            x_dims=120,

            z_dims=5,

        )



To train the Donut model:

To train the Donut model, and use a trained model for prediction:



.. code-block:: python



    from donut import DonutTrainer

    from donut import DonutTrainer, DonutPredictor



    trainer = DonutTrainer(model=model, model_vs=model_vs)

    with tf.Session().as_default():

        trainer.fit(train_values, train_labels, train_missing, mean, std)



To use a trained Donut model for prediction:



.. code-block:: python



    from donut import DonutPredictor



    predictor = DonutPredictor(model)

    with tf.Session().as_default():

        # Remember to train the model before using the predictor,

        # or to restore the saved model.

        ...



        # Now we can use the predictor.

    with tf.Session().as_default():

        trainer.fit(train_values, train_labels, train_missing, mean, std)

        test_score = predictor.get_score(test_values, test_missing)



To save and restore a trained model:

donut/model.py

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@@ -147,6 +147,10 @@ class Donut(VarScopeObject): 
        """

        Get the reconstruction probability for `x` and `y`.



        The larger `reconstruction probability`, the less likely a point

        is anomaly.  You may take the negative of the score, if you want

        something to directly indicate the severity of anomaly.



        Args:

            x (tf.Tensor): 2-D `float32` :class:`tf.Tensor`, the windows of

                KPI observations in a mini-batch.

donut/prediction.py

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@@ -95,6 +95,10 @@ class DonutPredictor(VarScopeObject): 
        """

        Get the `reconstruction probability` of specified KPI observations.



        The larger `reconstruction probability`, the less likely a point

        is anomaly.  You may take the negative of the score, if you want

        something to directly indicate the severity of anomaly.



        Args:

            values (np.ndarray): 1-D float32 array, the KPI observations.

            missing (np.ndarray): 1-D int32 array, the indicator of missing