.. _sec_textprediction_customization:

Text Prediction - Customized Hyperparameter Search
==================================================


This tutorial teaches you how to control the hyperparameter tuning
process in ``TextPrediction`` by specifying:

-  A custom search space of candidate hyperparameter values to consider.
-  Which hyperparameter optimization algorithm should be used to
   actually search through this space.

.. code:: python

    import numpy as np
    import warnings
    warnings.filterwarnings('ignore')
    np.random.seed(123)

Paraphrase Identification
-------------------------

We consider a Paraphrase Identification task for illustration. Given a
pair of sentences, the goal is to predict whether or not one sentence is
a restatement of the other (a binary classification task). Here we train
models on the `Microsoft Research Paraphrase
Corpus <https://www.microsoft.com/en-us/download/details.aspx?id=52398>`__
dataset.

.. code:: python

    from autogluon.utils.tabular.utils.loaders import load_pd
    
    train_data = load_pd.load('https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/train.parquet')
    dev_data = load_pd.load('https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/dev.parquet')
    train_data.head(10)


.. parsed-literal::
    :class: output

    Loaded data from: https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/train.parquet | Columns = 3 / 3 | Rows = 3668 -> 3668
    Loaded data from: https://autogluon-text.s3-accelerate.amazonaws.com/glue/mrpc/dev.parquet | Columns = 3 / 3 | Rows = 408 -> 408




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>sentence1</th>
          <th>sentence2</th>
          <th>label</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>Amrozi accused his brother , whom he called " ...</td>
          <td>Referring to him as only " the witness " , Amr...</td>
          <td>1</td>
        </tr>
        <tr>
          <th>1</th>
          <td>Yucaipa owned Dominick 's before selling the c...</td>
          <td>Yucaipa bought Dominick 's in 1995 for $ 693 m...</td>
          <td>0</td>
        </tr>
        <tr>
          <th>2</th>
          <td>They had published an advertisement on the Int...</td>
          <td>On June 10 , the ship 's owners had published ...</td>
          <td>1</td>
        </tr>
        <tr>
          <th>3</th>
          <td>Around 0335 GMT , Tab shares were up 19 cents ...</td>
          <td>Tab shares jumped 20 cents , or 4.6 % , to set...</td>
          <td>0</td>
        </tr>
        <tr>
          <th>4</th>
          <td>The stock rose $ 2.11 , or about 11 percent , ...</td>
          <td>PG &amp; E Corp. shares jumped $ 1.63 or 8 percent...</td>
          <td>1</td>
        </tr>
        <tr>
          <th>5</th>
          <td>Revenue in the first quarter of the year dropp...</td>
          <td>With the scandal hanging over Stewart 's compa...</td>
          <td>1</td>
        </tr>
        <tr>
          <th>6</th>
          <td>The Nasdaq had a weekly gain of 17.27 , or 1.2...</td>
          <td>The tech-laced Nasdaq Composite .IXIC rallied ...</td>
          <td>0</td>
        </tr>
        <tr>
          <th>7</th>
          <td>The DVD-CCA then appealed to the state Supreme...</td>
          <td>The DVD CCA appealed that decision to the U.S....</td>
          <td>1</td>
        </tr>
        <tr>
          <th>8</th>
          <td>That compared with $ 35.18 million , or 24 cen...</td>
          <td>Earnings were affected by a non-recurring $ 8 ...</td>
          <td>0</td>
        </tr>
        <tr>
          <th>9</th>
          <td>Shares of Genentech , a much larger company wi...</td>
          <td>Shares of Xoma fell 16 percent in early trade ...</td>
          <td>0</td>
        </tr>
      </tbody>
    </table>
    </div>



.. code:: python

    from autogluon_contrib_nlp.data.tokenizers import MosesTokenizer
    tokenizer = MosesTokenizer('en')  # just used to display sentences
    row_index = 2
    print('Paraphrase example:')
    print('Sentence1: ', tokenizer.decode(train_data['sentence1'][row_index].split()))
    print('Sentence2: ', tokenizer.decode(train_data['sentence2'][row_index].split()))
    print('Label: ', train_data['label'][row_index])
    
    row_index = 3
    print('\nNot Paraphrase example:')
    print('Sentence1:', tokenizer.decode(train_data['sentence1'][row_index].split()))
    print('Sentence2:', tokenizer.decode(train_data['sentence2'][row_index].split()))
    print('Label:', train_data['label'][row_index])


.. parsed-literal::
    :class: output

    /var/lib/jenkins/miniconda3/envs/autogluon_docs-v0_0_15/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
      and should_run_async(code)


.. parsed-literal::
    :class: output

    Paraphrase example:
    Sentence1:  They had published an advertisement on the Internet on June 10, offering the cargo for sale, he added.
    Sentence2:  On June 10, the ship's owners had published an advertisement on the Internet, offering the explosives for sale.
    Label:  1
    
    Not Paraphrase example:
    Sentence1: Around 0335 GMT, Tab shares were up 19 cents, or 4.4%, at A $4.56, having earlier set a record high of A $4.57.
    Sentence2: Tab shares jumped 20 cents, or 4.6%, to set a record closing high at A $4.57.
    Label: 0


Perform HPO over a Customized Search Space with Random Search
-------------------------------------------------------------

To control which hyperparameter values are considered during ``fit()``,
we specify the ``hyperparameters`` argument. Rather than specifying a
particular fixed value for a hyperparameter, we can specify a space of
values to search over via ``ag.space``. We can also specify which HPO
algorithm to use for the search via ``search_strategy`` (a simple
`random
search <https://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf>`__
is specified below). In this example, we search for good values of the
following hyperparameters:

-  warmup
-  learning rate
-  dropout before the first task-specific layer
-  layer-wise learning rate decay
-  number of task-specific layers

.. code:: python

    import autogluon as ag
    from autogluon import TextPrediction as task
    
    hyperparameters = {
        'models': {
                'BertForTextPredictionBasic': {
                    'search_space': {
                        'model.network.agg_net.num_layers': ag.space.Int(0, 3),
                        'model.network.agg_net.data_dropout': ag.space.Categorical(False, True),
                        'optimization.num_train_epochs': 4,
                        'optimization.warmup_portion': ag.space.Real(0.1, 0.2),
                        'optimization.layerwise_lr_decay': ag.space.Real(0.8, 1.0),
                        'optimization.lr': ag.space.Real(1E-5, 1E-4)
                    }
                },
        },
        'hpo_params': {
            'scheduler': 'fifo',  # schedule training jobs in a sequential first-in first-out fashion during HPO
            'search_strategy': 'random'  # perform HPO via simple random search
        }
    }

We can now call ``fit()`` with hyperparameter-tuning over our custom
search space. Below ``num_trials`` controls the maximal number of
different hyperparameter configurations for which AutoGluon will train
models (5 models are trained under different hyperparameter
configurations in this case). To achieve good performance in your
applications, you should use larger values of ``num_trials``, which may
identify superior hyperparameter values but will require longer
runtimes.

.. code:: python

    predictor_mrpc = task.fit(train_data,
                              label='label',
                              hyperparameters=hyperparameters,
                              num_trials=5,  # increase this to achieve good performance in your applications
                              time_limits=60 * 6,
                              ngpus_per_trial=1,
                              seed=123,
                              output_directory='./ag_mrpc_random_search')


.. parsed-literal::
    :class: output

    NumPy-shape semantics has been activated in your code. This is required for creating and manipulating scalar and zero-size tensors, which were not supported in MXNet before, as in the official NumPy library. Please DO NOT manually deactivate this semantics while using `mxnet.numpy` and `mxnet.numpy_extension` modules.
    2020-12-08 20:21:49,199 - root - INFO - All Logs will be saved to ./ag_mrpc_random_search/ag_text_prediction.log
    2020-12-08 20:21:49,215 - root - INFO - Train Dataset:
    2020-12-08 20:21:49,216 - root - INFO - Columns:
    
    - Text(
       name="sentence1"
       #total/missing=2934/0
       length, min/avg/max=38/118.03/226
    )
    - Text(
       name="sentence2"
       #total/missing=2934/0
       length, min/avg/max=42/118.07/215
    )
    - Categorical(
       name="label"
       #total/missing=2934/0
       num_class (total/non_special)=2/2
       categories=[0, 1]
       freq=[962, 1972]
    )
    
    
    2020-12-08 20:21:49,216 - root - INFO - Tuning Dataset:
    2020-12-08 20:21:49,216 - root - INFO - Columns:
    
    - Text(
       name="sentence1"
       #total/missing=734/0
       length, min/avg/max=40/120.27/209
    )
    - Text(
       name="sentence2"
       #total/missing=734/0
       length, min/avg/max=42/121.55/212
    )
    - Categorical(
       name="label"
       #total/missing=734/0
       num_class (total/non_special)=2/2
       categories=[0, 1]
       freq=[232, 502]
    )
    
    
    2020-12-08 20:21:49,217 - root - INFO - Label columns=['label'], Feature columns=['sentence1', 'sentence2'], Problem types=['classification'], Label shapes=[2]
    2020-12-08 20:21:49,217 - root - INFO - Eval Metric=acc, Stop Metric=acc, Log Metrics=['f1', 'mcc', 'auc', 'acc', 'nll']



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We can again evaluate our model's performance on separate test data.

.. code:: python

    dev_score = predictor_mrpc.evaluate(dev_data, metrics=['acc', 'f1'])
    print('Best Config = {}'.format(predictor_mrpc.results['best_config']))
    print('Total Time = {}s'.format(predictor_mrpc.results['total_time']))
    print('Accuracy = {:.2f}%'.format(dev_score['acc'] * 100))
    print('F1 = {:.2f}%'.format(dev_score['f1'] * 100))


.. parsed-literal::
    :class: output

    /var/lib/jenkins/miniconda3/envs/autogluon_docs-v0_0_15/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
      and should_run_async(code)


.. parsed-literal::
    :class: output

    Best Config = {'search_space▁model.network.agg_net.data_dropout▁choice': 1, 'search_space▁model.network.agg_net.num_layers': 2, 'search_space▁optimization.layerwise_lr_decay': 0.912355300099337, 'search_space▁optimization.lr': 7.14729072876085e-05, 'search_space▁optimization.warmup_portion': 0.19366799436501417}
    Total Time = 308.356942653656s
    Accuracy = 82.60%
    F1 = 87.99%


And also use the model to predict whether new sentence pairs are
paraphrases of each other or not.

.. code:: python

    sentence1 = 'It is simple to solve NLP problems with AutoGluon.'
    sentence2 = 'With AutoGluon, it is easy to solve NLP problems.'
    sentence3 = 'AutoGluon gives you a very bad user experience for solving NLP problems.'
    prediction1 = predictor_mrpc.predict({'sentence1': [sentence1], 'sentence2': [sentence2]})
    prediction1_prob = predictor_mrpc.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence2]})
    print('A = "{}"'.format(sentence1))
    print('B = "{}"'.format(sentence2))
    print('Prediction = "{}"'.format(prediction1[0] == 1))
    print('Prob = "{}"'.format(prediction1_prob[0]))
    print('')
    prediction2 = predictor_mrpc.predict({'sentence1': [sentence1], 'sentence2': [sentence3]})
    prediction2_prob = predictor_mrpc.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence3]})
    print('A = "{}"'.format(sentence1))
    print('B = "{}"'.format(sentence3))
    print('Prediction = "{}"'.format(prediction2[0] == 1))
    print('Prob = "{}"'.format(prediction2_prob[0]))


.. parsed-literal::
    :class: output

    A = "It is simple to solve NLP problems with AutoGluon."
    B = "With AutoGluon, it is easy to solve NLP problems."
    Prediction = "True"
    Prob = "[0.02651638 0.9734837 ]"
    
    A = "It is simple to solve NLP problems with AutoGluon."
    B = "AutoGluon gives you a very bad user experience for solving NLP problems."
    Prediction = "True"
    Prob = "[0.4781165 0.5218834]"


Use Bayesian Optimization
-------------------------

Instead of random search, we can perform HPO via `Bayesian
Optimization <https://distill.pub/2020/bayesian-optimization/>`__. Here
we specify **skopt** as the searcher, which uses a BayesOpt
implementation from the
`scikit-optimize <https://scikit-optimize.github.io/stable/>`__ library.

.. code:: python

    hyperparameters['hpo_params'] = {
        'scheduler': 'fifo',
        'search_strategy': 'skopt'
    }
    
    predictor_mrpc_skopt = task.fit(train_data, label='label',
                                    hyperparameters=hyperparameters,
                                    time_limits=60 * 6,
                                    num_trials=5,  # increase this to get good performance in your applications
                                    ngpus_per_trial=1, seed=123,
                                    output_directory='./ag_mrpc_custom_space_fifo_skopt')


.. parsed-literal::
    :class: output

    2020-12-08 20:27:07,092 - root - INFO - All Logs will be saved to ./ag_mrpc_custom_space_fifo_skopt/ag_text_prediction.log
    2020-12-08 20:27:07,109 - root - INFO - Train Dataset:
    2020-12-08 20:27:07,110 - root - INFO - Columns:
    
    - Text(
       name="sentence1"
       #total/missing=2934/0
       length, min/avg/max=38/118.38/226
    )
    - Text(
       name="sentence2"
       #total/missing=2934/0
       length, min/avg/max=42/118.63/215
    )
    - Categorical(
       name="label"
       #total/missing=2934/0
       num_class (total/non_special)=2/2
       categories=[0, 1]
       freq=[938, 1996]
    )
    
    
    2020-12-08 20:27:07,110 - root - INFO - Tuning Dataset:
    2020-12-08 20:27:07,111 - root - INFO - Columns:
    
    - Text(
       name="sentence1"
       #total/missing=734/0
       length, min/avg/max=38/118.90/215
    )
    - Text(
       name="sentence2"
       #total/missing=734/0
       length, min/avg/max=47/119.34/207
    )
    - Categorical(
       name="label"
       #total/missing=734/0
       num_class (total/non_special)=2/2
       categories=[0, 1]
       freq=[256, 478]
    )
    
    
    2020-12-08 20:27:07,111 - root - INFO - Label columns=['label'], Feature columns=['sentence1', 'sentence2'], Problem types=['classification'], Label shapes=[2]
    2020-12-08 20:27:07,112 - root - INFO - Eval Metric=acc, Stop Metric=acc, Log Metrics=['f1', 'mcc', 'auc', 'acc', 'nll']



.. parsed-literal::
    :class: output

    HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=5.0), HTML(value='')))


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.. parsed-literal::
    :class: output

    


.. parsed-literal::
    :class: output

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.. code:: python

    dev_score = predictor_mrpc_skopt.evaluate(dev_data, metrics=['acc', 'f1'])
    print('Best Config = {}'.format(predictor_mrpc_skopt.results['best_config']))
    print('Total Time = {}s'.format(predictor_mrpc_skopt.results['total_time']))
    print('Accuracy = {:.2f}%'.format(dev_score['acc'] * 100))
    print('F1 = {:.2f}%'.format(dev_score['f1'] * 100))


.. parsed-literal::
    :class: output

    Best Config = {'search_space▁model.network.agg_net.data_dropout▁choice': 1, 'search_space▁model.network.agg_net.num_layers': 1, 'search_space▁optimization.layerwise_lr_decay': 0.8833755385649641, 'search_space▁optimization.lr': 6.745308680620604e-05, 'search_space▁optimization.warmup_portion': 0.15989935817644294}
    Total Time = 415.89091873168945s
    Accuracy = 83.33%
    F1 = 88.47%


.. code:: python

    predictions = predictor_mrpc_skopt.predict(dev_data)
    prediction1 = predictor_mrpc_skopt.predict({'sentence1': [sentence1], 'sentence2': [sentence2]})
    prediction1_prob = predictor_mrpc_skopt.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence2]})
    print('A = "{}"'.format(sentence1))
    print('B = "{}"'.format(sentence2))
    print('Prediction = "{}"'.format(prediction1[0] == 1))
    print('Prob = "{}"'.format(prediction1_prob[0]))
    print('')
    prediction2 = predictor_mrpc_skopt.predict({'sentence1': [sentence1], 'sentence2': [sentence3]})
    prediction2_prob = predictor_mrpc_skopt.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence3]})
    print('A = "{}"'.format(sentence1))
    print('B = "{}"'.format(sentence3))
    print('Prediction = "{}"'.format(prediction2[0] == 1))
    print('Prob = "{}"'.format(prediction2_prob[0]))


.. parsed-literal::
    :class: output

    A = "It is simple to solve NLP problems with AutoGluon."
    B = "With AutoGluon, it is easy to solve NLP problems."
    Prediction = "True"
    Prob = "[0.0024036  0.99759644]"
    
    A = "It is simple to solve NLP problems with AutoGluon."
    B = "AutoGluon gives you a very bad user experience for solving NLP problems."
    Prediction = "False"
    Prob = "[0.78609246 0.21390754]"


Use Hyperband
-------------

Alternatively, we can instead use the `Hyperband
algorithm <https://arxiv.org/pdf/1603.06560.pdf>`__ for HPO. Hyperband
will try multiple hyperparameter configurations simultaneously and will
early stop training under poor configurations to free compute resources
for exploring new hyperparameter configurations. It may be able to
identify good hyperparameter values more quickly than other search
strategies in your applications.

.. code:: python

    hyperparameters['hpo_params'] = {
        'scheduler': 'hyperband',
        'search_strategy': 'random',
        'max_t': 40,  # Number of epochs per training run of one neural network
    }

.. code:: python

    predictor_mrpc_hyperband = task.fit(train_data, label='label',
                                        hyperparameters=hyperparameters,
                                        time_limits=60 * 6, ngpus_per_trial=1, seed=123,
                                        output_directory='./ag_mrpc_custom_space_hyperband')


.. parsed-literal::
    :class: output

    2020-12-08 20:34:21,669 - root - INFO - All Logs will be saved to ./ag_mrpc_custom_space_hyperband/ag_text_prediction.log
    2020-12-08 20:34:21,688 - root - INFO - Train Dataset:
    2020-12-08 20:34:21,688 - root - INFO - Columns:
    
    - Text(
       name="sentence1"
       #total/missing=2934/0
       length, min/avg/max=38/118.23/220
    )
    - Text(
       name="sentence2"
       #total/missing=2934/0
       length, min/avg/max=42/118.70/215
    )
    - Categorical(
       name="label"
       #total/missing=2934/0
       num_class (total/non_special)=2/2
       categories=[0, 1]
       freq=[960, 1974]
    )
    
    
    2020-12-08 20:34:21,689 - root - INFO - Tuning Dataset:
    2020-12-08 20:34:21,689 - root - INFO - Columns:
    
    - Text(
       name="sentence1"
       #total/missing=734/0
       length, min/avg/max=45/119.49/226
    )
    - Text(
       name="sentence2"
       #total/missing=734/0
       length, min/avg/max=46/119.05/210
    )
    - Categorical(
       name="label"
       #total/missing=734/0
       num_class (total/non_special)=2/2
       categories=[0, 1]
       freq=[234, 500]
    )
    
    
    2020-12-08 20:34:21,690 - root - INFO - Label columns=['label'], Feature columns=['sentence1', 'sentence2'], Problem types=['classification'], Label shapes=[2]
    2020-12-08 20:34:21,690 - root - INFO - Eval Metric=acc, Stop Metric=acc, Log Metrics=['f1', 'mcc', 'auc', 'acc', 'nll']
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.. code:: python

    dev_score = predictor_mrpc_hyperband.evaluate(dev_data, metrics=['acc', 'f1'])
    print('Best Config = {}'.format(predictor_mrpc_hyperband.results['best_config']))
    print('Total Time = {}s'.format(predictor_mrpc_hyperband.results['total_time']))
    print('Accuracy = {:.2f}%'.format(dev_score['acc'] * 100))
    print('F1 = {:.2f}%'.format(dev_score['f1'] * 100))


.. parsed-literal::
    :class: output

    Best Config = {'search_space▁model.network.agg_net.data_dropout▁choice': 0, 'search_space▁model.network.agg_net.num_layers': 2, 'search_space▁optimization.layerwise_lr_decay': 0.9, 'search_space▁optimization.lr': 5.5e-05, 'search_space▁optimization.warmup_portion': 0.15}
    Total Time = 457.0654797554016s
    Accuracy = 82.84%
    F1 = 88.22%


.. code:: python

    predictions = predictor_mrpc_hyperband.predict(dev_data)
    prediction1 = predictor_mrpc_hyperband.predict({'sentence1': [sentence1], 'sentence2': [sentence2]})
    prediction1_prob = predictor_mrpc_hyperband.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence2]})
    print('A = "{}"'.format(sentence1))
    print('B = "{}"'.format(sentence2))
    print('Prediction = "{}"'.format(prediction1[0] == 1))
    print('Prob = "{}"'.format(prediction1_prob[0]))
    print('')
    prediction2 = predictor_mrpc_hyperband.predict({'sentence1': [sentence1], 'sentence2': [sentence3]})
    prediction2_prob = predictor_mrpc_hyperband.predict_proba({'sentence1': [sentence1], 'sentence2': [sentence3]})
    print('A = "{}"'.format(sentence1))
    print('B = "{}"'.format(sentence3))
    print('Prediction = "{}"'.format(prediction2[0] == 1))
    print('Prob = "{}"'.format(prediction2_prob[0]))


.. parsed-literal::
    :class: output

    A = "It is simple to solve NLP problems with AutoGluon."
    B = "With AutoGluon, it is easy to solve NLP problems."
    Prediction = "True"
    Prob = "[0.01117248 0.9888276 ]"
    
    A = "It is simple to solve NLP problems with AutoGluon."
    B = "AutoGluon gives you a very bad user experience for solving NLP problems."
    Prediction = "False"
    Prob = "[0.76531047 0.23468953]"