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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text.ner.anago.wrapper</code></h1>
</header>
<section id="section-intro">
<p>Wrapper class.</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">&#34;&#34;&#34;
Wrapper class.
&#34;&#34;&#34;

from ....imports import *
from .. import metrics
from .models import BiLSTMCRF, load_model, save_model
from .preprocessing import IndexTransformer
from .tagger import Tagger
from .trainer import Trainer
from .utils import filter_embeddings


class Sequence(object):
    def __init__(
        self,
        word_embedding_dim=100,
        char_embedding_dim=25,
        word_lstm_size=100,
        char_lstm_size=25,
        fc_dim=100,
        dropout=0.5,
        embeddings=None,
        use_char=True,
        use_crf=True,
        initial_vocab=None,
        optimizer=&#34;adam&#34;,
    ):
        self.model = None
        self.p = None
        self.tagger = None

        self.word_embedding_dim = word_embedding_dim
        self.char_embedding_dim = char_embedding_dim
        self.word_lstm_size = word_lstm_size
        self.char_lstm_size = char_lstm_size
        self.fc_dim = fc_dim
        self.dropout = dropout
        self.embeddings = embeddings
        self.use_char = use_char
        self.use_crf = use_crf
        self.initial_vocab = initial_vocab
        self.optimizer = optimizer

    def fit(
        self,
        x_train,
        y_train,
        x_valid=None,
        y_valid=None,
        epochs=1,
        batch_size=32,
        verbose=1,
        callbacks=None,
        shuffle=True,
    ):
        &#34;&#34;&#34;Fit the model for a fixed number of epochs.

        Args:
            x_train: list of training data.
            y_train: list of training target (label) data.
            x_valid: list of validation data.
            y_valid: list of validation target (label) data.
            batch_size: Integer.
                Number of samples per gradient update.
                If unspecified, `batch_size` will default to 32.
            epochs: Integer. Number of epochs to train the model.
            verbose: Integer. 0, 1, or 2. Verbosity mode.
                0 = silent, 1 = progress bar, 2 = one line per epoch.
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during training.
            shuffle: Boolean (whether to shuffle the training data
                before each epoch). `shuffle` will default to True.
        &#34;&#34;&#34;
        p = IndexTransformer(initial_vocab=self.initial_vocab, use_char=self.use_char)
        p.fit(x_train, y_train)
        embeddings = filter_embeddings(
            self.embeddings, p._word_vocab.vocab, self.word_embedding_dim
        )

        model = BiLSTMCRF(
            char_vocab_size=p.char_vocab_size,
            word_vocab_size=p.word_vocab_size,
            num_labels=p.label_size,
            word_embedding_dim=self.word_embedding_dim,
            char_embedding_dim=self.char_embedding_dim,
            word_lstm_size=self.word_lstm_size,
            char_lstm_size=self.char_lstm_size,
            fc_dim=self.fc_dim,
            dropout=self.dropout,
            embeddings=embeddings,
            use_char=self.use_char,
            use_crf=self.use_crf,
        )
        model, loss = model.build()
        model.compile(loss=loss, optimizer=self.optimizer)

        trainer = Trainer(model, preprocessor=p)
        trainer.train(
            x_train,
            y_train,
            x_valid,
            y_valid,
            epochs=epochs,
            batch_size=batch_size,
            verbose=verbose,
            callbacks=callbacks,
            shuffle=shuffle,
        )

        self.p = p
        self.model = model

    def score(self, x_test, y_test):
        &#34;&#34;&#34;Returns the f1-micro score on the given test data and labels.

        Args:
            x_test : array-like, shape = (n_samples, sent_length)
            Test samples.

            y_test : array-like, shape = (n_samples, sent_length)
            True labels for x.

        Returns:
            score : float, f1-micro score.
        &#34;&#34;&#34;
        if self.model:
            x_test = self.p.transform(x_test)
            lengths = map(len, y_test)
            y_pred = self.model.predict(x_test)
            y_pred = self.p.inverse_transform(y_pred, lengths)
            score = metrics.f1_score(y_test, y_pred)
            return score
        else:
            raise OSError(&#34;Could not find a model. Call load(dir_path).&#34;)

    def analyze(self, text, tokenizer=str.split):
        &#34;&#34;&#34;Analyze text and return pretty format.

        Args:
            text: string, the input text.
            tokenizer: Tokenize input sentence. Default tokenizer is `str.split`.

        Returns:
            res: dict.
        &#34;&#34;&#34;
        if not self.tagger:
            self.tagger = Tagger(self.model, preprocessor=self.p, tokenizer=tokenizer)

        return self.tagger.analyze(text)

    def save(self, weights_file, params_file, preprocessor_file):
        self.p.save(preprocessor_file)
        save_model(self.model, weights_file, params_file)

    @classmethod
    def load(cls, weights_file, params_file, preprocessor_file):
        self = cls()
        self.p = IndexTransformer.load(preprocessor_file)
        self.model = load_model(weights_file, params_file)

        return self</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.text.ner.anago.wrapper.Sequence"><code class="flex name class">
<span>class <span class="ident">Sequence</span></span>
<span>(</span><span>word_embedding_dim=100, char_embedding_dim=25, word_lstm_size=100, char_lstm_size=25, fc_dim=100, dropout=0.5, embeddings=None, use_char=True, use_crf=True, initial_vocab=None, optimizer='adam')</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Sequence(object):
    def __init__(
        self,
        word_embedding_dim=100,
        char_embedding_dim=25,
        word_lstm_size=100,
        char_lstm_size=25,
        fc_dim=100,
        dropout=0.5,
        embeddings=None,
        use_char=True,
        use_crf=True,
        initial_vocab=None,
        optimizer=&#34;adam&#34;,
    ):
        self.model = None
        self.p = None
        self.tagger = None

        self.word_embedding_dim = word_embedding_dim
        self.char_embedding_dim = char_embedding_dim
        self.word_lstm_size = word_lstm_size
        self.char_lstm_size = char_lstm_size
        self.fc_dim = fc_dim
        self.dropout = dropout
        self.embeddings = embeddings
        self.use_char = use_char
        self.use_crf = use_crf
        self.initial_vocab = initial_vocab
        self.optimizer = optimizer

    def fit(
        self,
        x_train,
        y_train,
        x_valid=None,
        y_valid=None,
        epochs=1,
        batch_size=32,
        verbose=1,
        callbacks=None,
        shuffle=True,
    ):
        &#34;&#34;&#34;Fit the model for a fixed number of epochs.

        Args:
            x_train: list of training data.
            y_train: list of training target (label) data.
            x_valid: list of validation data.
            y_valid: list of validation target (label) data.
            batch_size: Integer.
                Number of samples per gradient update.
                If unspecified, `batch_size` will default to 32.
            epochs: Integer. Number of epochs to train the model.
            verbose: Integer. 0, 1, or 2. Verbosity mode.
                0 = silent, 1 = progress bar, 2 = one line per epoch.
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during training.
            shuffle: Boolean (whether to shuffle the training data
                before each epoch). `shuffle` will default to True.
        &#34;&#34;&#34;
        p = IndexTransformer(initial_vocab=self.initial_vocab, use_char=self.use_char)
        p.fit(x_train, y_train)
        embeddings = filter_embeddings(
            self.embeddings, p._word_vocab.vocab, self.word_embedding_dim
        )

        model = BiLSTMCRF(
            char_vocab_size=p.char_vocab_size,
            word_vocab_size=p.word_vocab_size,
            num_labels=p.label_size,
            word_embedding_dim=self.word_embedding_dim,
            char_embedding_dim=self.char_embedding_dim,
            word_lstm_size=self.word_lstm_size,
            char_lstm_size=self.char_lstm_size,
            fc_dim=self.fc_dim,
            dropout=self.dropout,
            embeddings=embeddings,
            use_char=self.use_char,
            use_crf=self.use_crf,
        )
        model, loss = model.build()
        model.compile(loss=loss, optimizer=self.optimizer)

        trainer = Trainer(model, preprocessor=p)
        trainer.train(
            x_train,
            y_train,
            x_valid,
            y_valid,
            epochs=epochs,
            batch_size=batch_size,
            verbose=verbose,
            callbacks=callbacks,
            shuffle=shuffle,
        )

        self.p = p
        self.model = model

    def score(self, x_test, y_test):
        &#34;&#34;&#34;Returns the f1-micro score on the given test data and labels.

        Args:
            x_test : array-like, shape = (n_samples, sent_length)
            Test samples.

            y_test : array-like, shape = (n_samples, sent_length)
            True labels for x.

        Returns:
            score : float, f1-micro score.
        &#34;&#34;&#34;
        if self.model:
            x_test = self.p.transform(x_test)
            lengths = map(len, y_test)
            y_pred = self.model.predict(x_test)
            y_pred = self.p.inverse_transform(y_pred, lengths)
            score = metrics.f1_score(y_test, y_pred)
            return score
        else:
            raise OSError(&#34;Could not find a model. Call load(dir_path).&#34;)

    def analyze(self, text, tokenizer=str.split):
        &#34;&#34;&#34;Analyze text and return pretty format.

        Args:
            text: string, the input text.
            tokenizer: Tokenize input sentence. Default tokenizer is `str.split`.

        Returns:
            res: dict.
        &#34;&#34;&#34;
        if not self.tagger:
            self.tagger = Tagger(self.model, preprocessor=self.p, tokenizer=tokenizer)

        return self.tagger.analyze(text)

    def save(self, weights_file, params_file, preprocessor_file):
        self.p.save(preprocessor_file)
        save_model(self.model, weights_file, params_file)

    @classmethod
    def load(cls, weights_file, params_file, preprocessor_file):
        self = cls()
        self.p = IndexTransformer.load(preprocessor_file)
        self.model = load_model(weights_file, params_file)

        return self</code></pre>
</details>
<h3>Static methods</h3>
<dl>
<dt id="ktrain.text.ner.anago.wrapper.Sequence.load"><code class="name flex">
<span>def <span class="ident">load</span></span>(<span>weights_file, params_file, preprocessor_file)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@classmethod
def load(cls, weights_file, params_file, preprocessor_file):
    self = cls()
    self.p = IndexTransformer.load(preprocessor_file)
    self.model = load_model(weights_file, params_file)

    return self</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.ner.anago.wrapper.Sequence.analyze"><code class="name flex">
<span>def <span class="ident">analyze</span></span>(<span>self, text, tokenizer=&lt;method &#x27;split&#x27; of &#x27;str&#x27; objects&gt;)</span>
</code></dt>
<dd>
<div class="desc"><p>Analyze text and return pretty format.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>text</code></strong></dt>
<dd>string, the input text.</dd>
<dt><strong><code>tokenizer</code></strong></dt>
<dd>Tokenize input sentence. Default tokenizer is <code>str.split</code>.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>res</code></dt>
<dd>dict.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def analyze(self, text, tokenizer=str.split):
    &#34;&#34;&#34;Analyze text and return pretty format.

    Args:
        text: string, the input text.
        tokenizer: Tokenize input sentence. Default tokenizer is `str.split`.

    Returns:
        res: dict.
    &#34;&#34;&#34;
    if not self.tagger:
        self.tagger = Tagger(self.model, preprocessor=self.p, tokenizer=tokenizer)

    return self.tagger.analyze(text)</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.anago.wrapper.Sequence.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train, x_valid=None, y_valid=None, epochs=1, batch_size=32, verbose=1, callbacks=None, shuffle=True)</span>
</code></dt>
<dd>
<div class="desc"><p>Fit the model for a fixed number of epochs.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>x_train</code></strong></dt>
<dd>list of training data.</dd>
<dt><strong><code>y_train</code></strong></dt>
<dd>list of training target (label) data.</dd>
<dt><strong><code>x_valid</code></strong></dt>
<dd>list of validation data.</dd>
<dt><strong><code>y_valid</code></strong></dt>
<dd>list of validation target (label) data.</dd>
<dt><strong><code>batch_size</code></strong></dt>
<dd>Integer.
Number of samples per gradient update.
If unspecified, <code>batch_size</code> will default to 32.</dd>
<dt><strong><code>epochs</code></strong></dt>
<dd>Integer. Number of epochs to train the model.</dd>
<dt><strong><code>verbose</code></strong></dt>
<dd>Integer. 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.</dd>
<dt><strong><code>callbacks</code></strong></dt>
<dd>List of <code>keras.callbacks.Callback</code> instances.
List of callbacks to apply during training.</dd>
<dt><strong><code>shuffle</code></strong></dt>
<dd>Boolean (whether to shuffle the training data
before each epoch). <code>shuffle</code> will default to True.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(
    self,
    x_train,
    y_train,
    x_valid=None,
    y_valid=None,
    epochs=1,
    batch_size=32,
    verbose=1,
    callbacks=None,
    shuffle=True,
):
    &#34;&#34;&#34;Fit the model for a fixed number of epochs.

    Args:
        x_train: list of training data.
        y_train: list of training target (label) data.
        x_valid: list of validation data.
        y_valid: list of validation target (label) data.
        batch_size: Integer.
            Number of samples per gradient update.
            If unspecified, `batch_size` will default to 32.
        epochs: Integer. Number of epochs to train the model.
        verbose: Integer. 0, 1, or 2. Verbosity mode.
            0 = silent, 1 = progress bar, 2 = one line per epoch.
        callbacks: List of `keras.callbacks.Callback` instances.
            List of callbacks to apply during training.
        shuffle: Boolean (whether to shuffle the training data
            before each epoch). `shuffle` will default to True.
    &#34;&#34;&#34;
    p = IndexTransformer(initial_vocab=self.initial_vocab, use_char=self.use_char)
    p.fit(x_train, y_train)
    embeddings = filter_embeddings(
        self.embeddings, p._word_vocab.vocab, self.word_embedding_dim
    )

    model = BiLSTMCRF(
        char_vocab_size=p.char_vocab_size,
        word_vocab_size=p.word_vocab_size,
        num_labels=p.label_size,
        word_embedding_dim=self.word_embedding_dim,
        char_embedding_dim=self.char_embedding_dim,
        word_lstm_size=self.word_lstm_size,
        char_lstm_size=self.char_lstm_size,
        fc_dim=self.fc_dim,
        dropout=self.dropout,
        embeddings=embeddings,
        use_char=self.use_char,
        use_crf=self.use_crf,
    )
    model, loss = model.build()
    model.compile(loss=loss, optimizer=self.optimizer)

    trainer = Trainer(model, preprocessor=p)
    trainer.train(
        x_train,
        y_train,
        x_valid,
        y_valid,
        epochs=epochs,
        batch_size=batch_size,
        verbose=verbose,
        callbacks=callbacks,
        shuffle=shuffle,
    )

    self.p = p
    self.model = model</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.anago.wrapper.Sequence.save"><code class="name flex">
<span>def <span class="ident">save</span></span>(<span>self, weights_file, params_file, preprocessor_file)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def save(self, weights_file, params_file, preprocessor_file):
    self.p.save(preprocessor_file)
    save_model(self.model, weights_file, params_file)</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.anago.wrapper.Sequence.score"><code class="name flex">
<span>def <span class="ident">score</span></span>(<span>self, x_test, y_test)</span>
</code></dt>
<dd>
<div class="desc"><p>Returns the f1-micro score on the given test data and labels.</p>
<h2 id="args">Args</h2>
<p>x_test : array-like, shape = (n_samples, sent_length)
Test samples.</p>
<p>y_test : array-like, shape = (n_samples, sent_length)
True labels for x.</p>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>score </code></dt>
<dd>float, f1-micro score.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def score(self, x_test, y_test):
    &#34;&#34;&#34;Returns the f1-micro score on the given test data and labels.

    Args:
        x_test : array-like, shape = (n_samples, sent_length)
        Test samples.

        y_test : array-like, shape = (n_samples, sent_length)
        True labels for x.

    Returns:
        score : float, f1-micro score.
    &#34;&#34;&#34;
    if self.model:
        x_test = self.p.transform(x_test)
        lengths = map(len, y_test)
        y_pred = self.model.predict(x_test)
        y_pred = self.p.inverse_transform(y_pred, lengths)
        score = metrics.f1_score(y_test, y_pred)
        return score
    else:
        raise OSError(&#34;Could not find a model. Call load(dir_path).&#34;)</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
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<h1>Index</h1>
<div class="toc">
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<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.text.ner.anago" href="index.html">ktrain.text.ner.anago</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.text.ner.anago.wrapper.Sequence" href="#ktrain.text.ner.anago.wrapper.Sequence">Sequence</a></code></h4>
<ul class="">
<li><code><a title="ktrain.text.ner.anago.wrapper.Sequence.analyze" href="#ktrain.text.ner.anago.wrapper.Sequence.analyze">analyze</a></code></li>
<li><code><a title="ktrain.text.ner.anago.wrapper.Sequence.fit" href="#ktrain.text.ner.anago.wrapper.Sequence.fit">fit</a></code></li>
<li><code><a title="ktrain.text.ner.anago.wrapper.Sequence.load" href="#ktrain.text.ner.anago.wrapper.Sequence.load">load</a></code></li>
<li><code><a title="ktrain.text.ner.anago.wrapper.Sequence.save" href="#ktrain.text.ner.anago.wrapper.Sequence.save">save</a></code></li>
<li><code><a title="ktrain.text.ner.anago.wrapper.Sequence.score" href="#ktrain.text.ner.anago.wrapper.Sequence.score">score</a></code></li>
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