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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text.shallownlp</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .classifier import Classifier
from .searcher import *
from .ner import NER
from .utils import sent_tokenize, extract_filenames, read_text
__all__ = ['Classifier',
'Searcher', 'search', 'find_chinese', 'find_arabic', 'find_russian', 'read_text',
'NER',
'sent_tokenize', 'extract_filenames', 'read_text']</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="ktrain.text.shallownlp.classifier" href="classifier.html">ktrain.text.shallownlp.classifier</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.shallownlp.imports" href="imports.html">ktrain.text.shallownlp.imports</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.shallownlp.ner" href="ner.html">ktrain.text.shallownlp.ner</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.shallownlp.searcher" href="searcher.html">ktrain.text.shallownlp.searcher</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.shallownlp.utils" href="utils.html">ktrain.text.shallownlp.utils</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.text.shallownlp.extract_filenames"><code class="name flex">
<span>def <span class="ident">extract_filenames</span></span>(<span>corpus_path, follow_links=False)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def extract_filenames(corpus_path, follow_links=False):
if os.listdir(corpus_path) == []:
raise ValueError("%s: path is empty" % corpus_path)
for root, _, fnames in os.walk(corpus_path, followlinks=follow_links):
for filename in fnames:
try:
yield os.path.join(root, filename)
except Exception:
continue</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.find_arabic"><code class="name flex">
<span>def <span class="ident">find_arabic</span></span>(<span>s)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def find_arabic(s): return re.findall(r'[\u0600-\u06FF]+', s)</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.find_chinese"><code class="name flex">
<span>def <span class="ident">find_chinese</span></span>(<span>s)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def find_chinese(s): return re.findall(r'[\u4e00-\u9fff]+', s)</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.find_russian"><code class="name flex">
<span>def <span class="ident">find_russian</span></span>(<span>s)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def find_russian(s): return find_cyrillic(s)</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.read_text"><code class="name flex">
<span>def <span class="ident">read_text</span></span>(<span>filename)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def read_text(filename):
with open(filename, 'rb') as f:
text = f.read()
encoding = detect_encoding([text])
try:
decoded_text = text.decode(encoding)
except:
U.vprint('Decoding with %s failed 1st attempt - using %s with skips' % (encoding,
encoding),
verbose=verbose)
decoded_text = decode_by_line(text, encoding=encoding)
return decoded_text.strip()</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.search"><code class="name flex">
<span>def <span class="ident">search</span></span>(<span>query, doc, case_sensitive=False, keys=[], progress=False)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def search(query, doc, case_sensitive=False, keys=[], progress=False):
searcher = Searcher(query)
return searcher.search(doc, case_sensitive=case_sensitive, keys=keys, progress=progress)</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.sent_tokenize"><code class="name flex">
<span>def <span class="ident">sent_tokenize</span></span>(<span>text)</span>
</code></dt>
<dd>
<div class="desc"><p>segment text into sentences</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def sent_tokenize(text):
"""
segment text into sentences
"""
lang = detect_lang(text)
sents = []
if is_chinese(lang):
for sent in re.findall(u'[^!?。\.\!\?]+[!?。\.\!\?]?', text, flags=re.U):
sents.append(sent)
else:
for paragraph in segmenter.process(text):
for sentence in paragraph:
sents.append(" ".join([t.value for t in sentence]))
return sents</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.text.shallownlp.Classifier"><code class="flex name class">
<span>class <span class="ident">Classifier</span></span>
<span>(</span><span>model=None)</span>
</code></dt>
<dd>
<div class="desc"><p>instantiate a classifier with an optional previously-saved model</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Classifier:
def __init__(self, model=None):
"""
instantiate a classifier with an optional previously-saved model
"""
self.model = None
def create_model(self, ctype, texts, hp_dict={}, ngram_range=(1,3), binary=True):
"""
```
create a model
Args:
ctype(str): one of {'nbsvm', 'logreg', 'sgdclassifier'}
texts(list): list of texts
hp_dict(dict): dictionary of hyperparameters to use for the ctype selected.
hp_dict can also be used to supply arguments to CountVectorizer
ngram_range(tuple): default ngram_range.
overridden if 'ngram_range' in hp_dict
binary(bool): default value for binary argument to CountVectorizer.
overridden if 'binary' key in hp_dict
```
"""
lang = U.detect_lang(texts)
if U.is_chinese(lang):
token_pattern = r'(?u)\b\w+\b'
else:
token_pattern = r'\w+|[%s]' % string.punctuation
if ctype == 'nbsvm':
clf = NBSVM(C=hp_dict.get('C', 0.01),
alpha=hp_dict.get('alpha', 0.75),
beta=hp_dict.get('beta', 0.25),
fit_intercept=hp_dict.get('fit_intercept', False))
elif ctype=='logreg':
clf = LogisticRegression(C=hp_dict.get('C', 0.1),
dual=hp_dict.get('dual', True),
penalty=hp_dict.get('penalty', 'l2'),
tol=hp_dict.get('tol', 1e-4),
intercept_scaling=hp_dict.get('intercept_scaling', 1),
solver=hp_dict.get('solver', 'liblinear'),
max_iter=hp_dict.get('max_iter', 100),
multi_class=hp_dict.get('multi_class', 'auto'),
warm_start=hp_dict.get('warm_start', False),
n_jobs=hp_dict.get('n_jobs', None),
l1_ratio=hp_dict.get('l1_ratio', None),
random_state=hp_dict.get('random_state', 42),
class_weight=hp_dict.get('class_weight', None)
)
elif ctype == 'sgdclassifier':
clf = SGDClassifier(loss=hp_dict.get('loss', 'hinge'),
penalty=hp_dict.get('penalty', 'l2'),
alpha=hp_dict.get('alpha', 1e-3),
random_state=hp_dict.get('random_state', 42),
max_iter=hp_dict.get('max_iter', 5), # scikit-learn default is 1000
tol=hp_dict.get('tol', None),
l1_ratio=hp_dict.get('l1_ratio', 0.15),
fit_intercept=hp_dict.get('fit_intercept', True),
episilon=hp_dict.get('epsilon', 0.1),
n_jobs=hp_dict.get('n_jobs', None),
learning_rate=hp_dict.get('learning_rate', 'optimal'),
eta0=hp_dict.get('eta0', 0.0),
power_t=hp_dict.get('power_t', 0.5),
early_stopping=hp_dict.get('early_stopping', False),
validation_fraction=hp_dict.get('validation_fraction', 0.1),
n_iter_no_change=hp_dict.get('n_iter_no_change', 5),
warm_start=hp_dict.get('warm_start', False),
average=hp_dict.get('average', False),
class_weight=hp_dict.get('class_weight', None))
else:
raise ValueError('Unknown ctype: %s' % (ctype))
self.model = Pipeline([ ('vect', CountVectorizer(ngram_range=hp_dict.get('ngram_range', ngram_range),
binary=hp_dict.get('binary', binary),
token_pattern=token_pattern,
max_features=hp_dict.get('max_features', None),
max_df=hp_dict.get('max_df', 1.0),
min_df=hp_dict.get('min_df', 1),
stop_words=hp_dict.get('stop_words', None),
lowercase=hp_dict.get('lowercase', True),
strip_accents=hp_dict.get('strip_accents', None),
encoding=hp_dict.get('encoding', 'utf-8')
)),
('clf', clf) ])
return
@classmethod
def load_texts_from_folder(cls, folder_path,
subfolders=None,
shuffle=True,
encoding=None):
"""
```
load text files from folder
Args:
folder_path(str): path to folder containing documents
The supplied folder should contain a subfolder
for each category, which will be used as the class label
subfolders(list): list of subfolders under folder_path to consider
Example: If folder_path contains subfolders pos, neg, and
unlabeled, then unlabeled folder can be ignored by
setting subfolders=['pos', 'neg']
shuffle(bool): If True, list of texts will be shuffled
encoding(str): encoding to use. default:None (auto-detected)
Returns:
tuple: (texts, labels, label_names)
```
"""
bunch = load_files(folder_path, categories=subfolders, shuffle=shuffle)
texts = bunch.data
labels = bunch.target
label_names = bunch.target_names
#print('target names:')
#for idx, label_name in enumerate(bunch.target_names):
#print('\t%s:%s' % (idx, label_name))
# decode based on supplied encoding
if encoding is None:
encoding = U.detect_encoding(texts)
if encoding != 'utf-8':
print('detected encoding: %s' % (encoding))
try:
texts = [text.decode(encoding) for text in texts]
except:
print('Decoding with %s failed 1st attempt - using %s with skips' % (encoding,
encoding))
texts = U.decode_by_line(texts, encoding=encoding)
return (texts, labels, label_names)
@classmethod
def load_texts_from_csv(cls, csv_filepath, text_column='text', label_column='label',
sep=',', encoding=None):
"""
```
load text files from csv file
CSV should have at least two columns.
Example:
Text | Label
I love this movie. | positive
I hated this movie.| negative
Args:
csv_filepath(str): path to CSV file
text_column(str): name of column containing the texts. default:'text'
label_column(str): name of column containing the labels in string format
default:'label'
sep(str): character that separates columns in CSV. default:','
encoding(str): encoding to use. default:None (auto-detected)
Returns:
tuple: (texts, labels, label_names)
```
"""
if encoding is None:
with open(csv_filepath, 'rb') as f:
encoding = U.detect_encoding([f.read()])
if encoding != 'utf-8':
print('detected encoding: %s (if wrong, set manually)' % (encoding))
import pandas as pd
df = pd.read_csv(csv_filepath, encoding=encoding, sep=sep)
texts = df[text_column].fillna('fillna').values
labels = df[label_column].values
le = LabelEncoder()
le.fit(labels)
labels = le.transform(labels)
return (texts, labels, le.classes_)
def fit(self, x_train, y_train, ctype='logreg'):
"""
```
train a classifier
Args:
x_train(list or np.ndarray): training texts
y_train(np.ndarray): training labels
ctype(str): One of {'logreg', 'nbsvm', 'sgdclassifier'}. default:nbsvm
```
"""
lang = U.detect_lang(x_train)
if U.is_chinese(lang):
x_train = U.split_chinese(x_train)
if self.model is None:
self.create_model(ctype, x_train)
self.model.fit(x_train, y_train)
return self
def predict(self, x_test, return_proba=False):
"""
```
make predictions on text data
Args:
x_test(list or np.ndarray or str): array of texts on which to make predictions or a string representing text
```
"""
if return_proba and not hasattr(self.model['clf'], 'predict_proba'):
raise ValueError('%s does not support predict_proba' % (type(self.model['clf']).__name__))
if isinstance(x_test, str): x_test = [x_test]
lang = U.detect_lang(x_test)
if U.is_chinese(lang): x_test = U.split_chinese(x_test)
if self.model is None: raise ValueError('model is None - call fit or load to set the model')
if return_proba:
predicted = self.model.predict_proba(x_test)
else:
predicted = self.model.predict(x_test)
if len(predicted) == 1: predicted = predicted[0]
return predicted
def predict_proba(self, x_test):
"""
predict_proba
"""
return self.predict(x_test, return_proba=True)
def evaluate(self, x_test, y_test):
"""
```
evaluate
Args:
x_test(list or np.ndarray): training texts
y_test(np.ndarray): training labels
```
"""
predicted = self.predict(x_test)
return np.mean(predicted == y_test)
def save(self, filename):
"""
save model
"""
dump(self.model, filename)
def load(self, filename):
"""
load model
"""
self.model = load(filename)
def grid_search(self, params, x_train, y_train, n_jobs=-1):
"""
```
Performs grid search to find optimal set of hyperparameters
Args:
params (dict): A dictionary defining the space of the search.
Example for finding optimal value of alpha in NBSVM:
parameters = {
#'clf__C': (1e0, 1e-1, 1e-2),
'clf__alpha': (0.1, 0.2, 0.4, 0.5, 0.75, 0.9, 1.0),
#'clf__fit_intercept': (True, False),
#'clf__beta' : (0.1, 0.25, 0.5, 0.9)
}
n_jobs(int): number of jobs to run in parallel. default:-1 (use all processors)
```
"""
gs_clf = GridSearchCV(self.model, params, n_jobs=n_jobs)
gs_clf = gs_clf.fit(x_train, y_train)
#gs_clf.best_score_
for param_name in sorted(params.keys()):
print("%s: %r" % (param_name, gs_clf.best_params_[param_name]))
return</code></pre>
</details>
<h3>Static methods</h3>
<dl>
<dt id="ktrain.text.shallownlp.Classifier.load_texts_from_csv"><code class="name flex">
<span>def <span class="ident">load_texts_from_csv</span></span>(<span>csv_filepath, text_column='text', label_column='label', sep=',', encoding=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>load text files from csv file
CSV should have at least two columns.
Example:
Text | Label
I love this movie. | positive
I hated this movie.| negative
Args:
csv_filepath(str): path to CSV file
text_column(str): name of column containing the texts. default:'text'
label_column(str): name of column containing the labels in string format
default:'label'
sep(str): character that separates columns in CSV. default:','
encoding(str): encoding to use. default:None (auto-detected)
Returns:
tuple: (texts, labels, label_names)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@classmethod
def load_texts_from_csv(cls, csv_filepath, text_column='text', label_column='label',
sep=',', encoding=None):
"""
```
load text files from csv file
CSV should have at least two columns.
Example:
Text | Label
I love this movie. | positive
I hated this movie.| negative
Args:
csv_filepath(str): path to CSV file
text_column(str): name of column containing the texts. default:'text'
label_column(str): name of column containing the labels in string format
default:'label'
sep(str): character that separates columns in CSV. default:','
encoding(str): encoding to use. default:None (auto-detected)
Returns:
tuple: (texts, labels, label_names)
```
"""
if encoding is None:
with open(csv_filepath, 'rb') as f:
encoding = U.detect_encoding([f.read()])
if encoding != 'utf-8':
print('detected encoding: %s (if wrong, set manually)' % (encoding))
import pandas as pd
df = pd.read_csv(csv_filepath, encoding=encoding, sep=sep)
texts = df[text_column].fillna('fillna').values
labels = df[label_column].values
le = LabelEncoder()
le.fit(labels)
labels = le.transform(labels)
return (texts, labels, le.classes_)</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.Classifier.load_texts_from_folder"><code class="name flex">
<span>def <span class="ident">load_texts_from_folder</span></span>(<span>folder_path, subfolders=None, shuffle=True, encoding=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>load text files from folder
Args:
folder_path(str): path to folder containing documents
The supplied folder should contain a subfolder
for each category, which will be used as the class label
subfolders(list): list of subfolders under folder_path to consider
Example: If folder_path contains subfolders pos, neg, and
unlabeled, then unlabeled folder can be ignored by
setting subfolders=['pos', 'neg']
shuffle(bool): If True, list of texts will be shuffled
encoding(str): encoding to use. default:None (auto-detected)
Returns:
tuple: (texts, labels, label_names)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@classmethod
def load_texts_from_folder(cls, folder_path,
subfolders=None,
shuffle=True,
encoding=None):
"""
```
load text files from folder
Args:
folder_path(str): path to folder containing documents
The supplied folder should contain a subfolder
for each category, which will be used as the class label
subfolders(list): list of subfolders under folder_path to consider
Example: If folder_path contains subfolders pos, neg, and
unlabeled, then unlabeled folder can be ignored by
setting subfolders=['pos', 'neg']
shuffle(bool): If True, list of texts will be shuffled
encoding(str): encoding to use. default:None (auto-detected)
Returns:
tuple: (texts, labels, label_names)
```
"""
bunch = load_files(folder_path, categories=subfolders, shuffle=shuffle)
texts = bunch.data
labels = bunch.target
label_names = bunch.target_names
#print('target names:')
#for idx, label_name in enumerate(bunch.target_names):
#print('\t%s:%s' % (idx, label_name))
# decode based on supplied encoding
if encoding is None:
encoding = U.detect_encoding(texts)
if encoding != 'utf-8':
print('detected encoding: %s' % (encoding))
try:
texts = [text.decode(encoding) for text in texts]
except:
print('Decoding with %s failed 1st attempt - using %s with skips' % (encoding,
encoding))
texts = U.decode_by_line(texts, encoding=encoding)
return (texts, labels, label_names)</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.shallownlp.Classifier.create_model"><code class="name flex">
<span>def <span class="ident">create_model</span></span>(<span>self, ctype, texts, hp_dict={}, ngram_range=(1, 3), binary=True)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>create a model
Args:
ctype(str): one of {'nbsvm', 'logreg', 'sgdclassifier'}
texts(list): list of texts
hp_dict(dict): dictionary of hyperparameters to use for the ctype selected.
hp_dict can also be used to supply arguments to CountVectorizer
ngram_range(tuple): default ngram_range.
overridden if 'ngram_range' in hp_dict
binary(bool): default value for binary argument to CountVectorizer.
overridden if 'binary' key in hp_dict
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def create_model(self, ctype, texts, hp_dict={}, ngram_range=(1,3), binary=True):
"""
```
create a model
Args:
ctype(str): one of {'nbsvm', 'logreg', 'sgdclassifier'}
texts(list): list of texts
hp_dict(dict): dictionary of hyperparameters to use for the ctype selected.
hp_dict can also be used to supply arguments to CountVectorizer
ngram_range(tuple): default ngram_range.
overridden if 'ngram_range' in hp_dict
binary(bool): default value for binary argument to CountVectorizer.
overridden if 'binary' key in hp_dict
```
"""
lang = U.detect_lang(texts)
if U.is_chinese(lang):
token_pattern = r'(?u)\b\w+\b'
else:
token_pattern = r'\w+|[%s]' % string.punctuation
if ctype == 'nbsvm':
clf = NBSVM(C=hp_dict.get('C', 0.01),
alpha=hp_dict.get('alpha', 0.75),
beta=hp_dict.get('beta', 0.25),
fit_intercept=hp_dict.get('fit_intercept', False))
elif ctype=='logreg':
clf = LogisticRegression(C=hp_dict.get('C', 0.1),
dual=hp_dict.get('dual', True),
penalty=hp_dict.get('penalty', 'l2'),
tol=hp_dict.get('tol', 1e-4),
intercept_scaling=hp_dict.get('intercept_scaling', 1),
solver=hp_dict.get('solver', 'liblinear'),
max_iter=hp_dict.get('max_iter', 100),
multi_class=hp_dict.get('multi_class', 'auto'),
warm_start=hp_dict.get('warm_start', False),
n_jobs=hp_dict.get('n_jobs', None),
l1_ratio=hp_dict.get('l1_ratio', None),
random_state=hp_dict.get('random_state', 42),
class_weight=hp_dict.get('class_weight', None)
)
elif ctype == 'sgdclassifier':
clf = SGDClassifier(loss=hp_dict.get('loss', 'hinge'),
penalty=hp_dict.get('penalty', 'l2'),
alpha=hp_dict.get('alpha', 1e-3),
random_state=hp_dict.get('random_state', 42),
max_iter=hp_dict.get('max_iter', 5), # scikit-learn default is 1000
tol=hp_dict.get('tol', None),
l1_ratio=hp_dict.get('l1_ratio', 0.15),
fit_intercept=hp_dict.get('fit_intercept', True),
episilon=hp_dict.get('epsilon', 0.1),
n_jobs=hp_dict.get('n_jobs', None),
learning_rate=hp_dict.get('learning_rate', 'optimal'),
eta0=hp_dict.get('eta0', 0.0),
power_t=hp_dict.get('power_t', 0.5),
early_stopping=hp_dict.get('early_stopping', False),
validation_fraction=hp_dict.get('validation_fraction', 0.1),
n_iter_no_change=hp_dict.get('n_iter_no_change', 5),
warm_start=hp_dict.get('warm_start', False),
average=hp_dict.get('average', False),
class_weight=hp_dict.get('class_weight', None))
else:
raise ValueError('Unknown ctype: %s' % (ctype))
self.model = Pipeline([ ('vect', CountVectorizer(ngram_range=hp_dict.get('ngram_range', ngram_range),
binary=hp_dict.get('binary', binary),
token_pattern=token_pattern,
max_features=hp_dict.get('max_features', None),
max_df=hp_dict.get('max_df', 1.0),
min_df=hp_dict.get('min_df', 1),
stop_words=hp_dict.get('stop_words', None),
lowercase=hp_dict.get('lowercase', True),
strip_accents=hp_dict.get('strip_accents', None),
encoding=hp_dict.get('encoding', 'utf-8')
)),
('clf', clf) ])
return</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.Classifier.evaluate"><code class="name flex">
<span>def <span class="ident">evaluate</span></span>(<span>self, x_test, y_test)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>evaluate
Args:
x_test(list or np.ndarray): training texts
y_test(np.ndarray): training labels
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def evaluate(self, x_test, y_test):
"""
```
evaluate
Args:
x_test(list or np.ndarray): training texts
y_test(np.ndarray): training labels
```
"""
predicted = self.predict(x_test)
return np.mean(predicted == y_test)</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.Classifier.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train, ctype='logreg')</span>
</code></dt>
<dd>
<div class="desc"><pre><code>train a classifier
Args:
x_train(list or np.ndarray): training texts
y_train(np.ndarray): training labels
ctype(str): One of {'logreg', 'nbsvm', 'sgdclassifier'}. default:nbsvm
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, x_train, y_train, ctype='logreg'):
"""
```
train a classifier
Args:
x_train(list or np.ndarray): training texts
y_train(np.ndarray): training labels
ctype(str): One of {'logreg', 'nbsvm', 'sgdclassifier'}. default:nbsvm
```
"""
lang = U.detect_lang(x_train)
if U.is_chinese(lang):
x_train = U.split_chinese(x_train)
if self.model is None:
self.create_model(ctype, x_train)
self.model.fit(x_train, y_train)
return self</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.Classifier.grid_search"><code class="name flex">
<span>def <span class="ident">grid_search</span></span>(<span>self, params, x_train, y_train, n_jobs=-1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Performs grid search to find optimal set of hyperparameters
Args:
params (dict): A dictionary defining the space of the search.
Example for finding optimal value of alpha in NBSVM:
parameters = {
#'clf__C': (1e0, 1e-1, 1e-2),
'clf__alpha': (0.1, 0.2, 0.4, 0.5, 0.75, 0.9, 1.0),
#'clf__fit_intercept': (True, False),
#'clf__beta' : (0.1, 0.25, 0.5, 0.9)
}
n_jobs(int): number of jobs to run in parallel. default:-1 (use all processors)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def grid_search(self, params, x_train, y_train, n_jobs=-1):
"""
```
Performs grid search to find optimal set of hyperparameters
Args:
params (dict): A dictionary defining the space of the search.
Example for finding optimal value of alpha in NBSVM:
parameters = {
#'clf__C': (1e0, 1e-1, 1e-2),
'clf__alpha': (0.1, 0.2, 0.4, 0.5, 0.75, 0.9, 1.0),
#'clf__fit_intercept': (True, False),
#'clf__beta' : (0.1, 0.25, 0.5, 0.9)
}
n_jobs(int): number of jobs to run in parallel. default:-1 (use all processors)
```
"""
gs_clf = GridSearchCV(self.model, params, n_jobs=n_jobs)
gs_clf = gs_clf.fit(x_train, y_train)
#gs_clf.best_score_
for param_name in sorted(params.keys()):
print("%s: %r" % (param_name, gs_clf.best_params_[param_name]))
return</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.Classifier.load"><code class="name flex">
<span>def <span class="ident">load</span></span>(<span>self, filename)</span>
</code></dt>
<dd>
<div class="desc"><p>load model</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def load(self, filename):
"""
load model
"""
self.model = load(filename)</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.Classifier.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, x_test, return_proba=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>make predictions on text data
Args:
x_test(list or np.ndarray or str): array of texts on which to make predictions or a string representing text
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, x_test, return_proba=False):
"""
```
make predictions on text data
Args:
x_test(list or np.ndarray or str): array of texts on which to make predictions or a string representing text
```
"""
if return_proba and not hasattr(self.model['clf'], 'predict_proba'):
raise ValueError('%s does not support predict_proba' % (type(self.model['clf']).__name__))
if isinstance(x_test, str): x_test = [x_test]
lang = U.detect_lang(x_test)
if U.is_chinese(lang): x_test = U.split_chinese(x_test)
if self.model is None: raise ValueError('model is None - call fit or load to set the model')
if return_proba:
predicted = self.model.predict_proba(x_test)
else:
predicted = self.model.predict(x_test)
if len(predicted) == 1: predicted = predicted[0]
return predicted</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.Classifier.predict_proba"><code class="name flex">
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
</code></dt>
<dd>
<div class="desc"><p>predict_proba</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_proba(self, x_test):
"""
predict_proba
"""
return self.predict(x_test, return_proba=True)</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.Classifier.save"><code class="name flex">
<span>def <span class="ident">save</span></span>(<span>self, filename)</span>
</code></dt>
<dd>
<div class="desc"><p>save model</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def save(self, filename):
"""
save model
"""
dump(self.model, filename)</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="ktrain.text.shallownlp.NER"><code class="flex name class">
<span>class <span class="ident">NER</span></span>
<span>(</span><span>lang='en', predictor_path=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>pretrained NER.
Only English and Chinese are currenty supported.
Args:
lang(str): Currently, one of {'en', 'zh', 'ru'}: en=English , zh=Chinese, or ru=Russian
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class NER:
def __init__(self, lang='en', predictor_path=None):
"""
```
pretrained NER.
Only English and Chinese are currenty supported.
Args:
lang(str): Currently, one of {'en', 'zh', 'ru'}: en=English , zh=Chinese, or ru=Russian
```
"""
if lang is None:
raise ValueError('lang is required (e.g., "en" for English, "zh" for Chinese, "ru" for Russian, etc.')
if predictor_path is None and lang not in ['en', 'zh', 'ru']:
raise ValueError("Unsupported language: if predictor_path is None, then lang must be " +\
"'en' for English, 'zh' for Chinese, or 'ru' for Chinese")
self.lang = lang
if os.environ.get('DISABLE_V2_BEHAVIOR', None) != '1':
warnings.warn("Please add os.environ['DISABLE_V2_BEHAVIOR'] = '1' at top of your script or notebook")
msg = "\nNER in ktrain uses the CRF module from keras_contrib, which is not yet\n" +\
"fully compatible with TensorFlow 2. To use NER, you must add the following to the top of your\n" +\
"script or notebook BEFORE you import ktrain (after restarting runtime):\n\n" +\
"import os\n" +\
"os.environ['DISABLE_V2_BEHAVIOR'] = '1'\n"
print(msg)
return
else:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
if predictor_path is None and self.lang == 'zh':
dirpath = os.path.dirname(os.path.abspath(__file__))
fpath = os.path.join(dirpath, 'ner_models/ner_chinese')
elif predictor_path is None and self.lang == 'ru':
dirpath = os.path.dirname(os.path.abspath(__file__))
fpath = os.path.join(dirpath, 'ner_models/ner_russian')
elif predictor_path is None and self.lang=='en':
dirpath = os.path.dirname(os.path.abspath(__file__))
fpath = os.path.join(dirpath, 'ner_models/ner_english')
elif predictor_path is None:
raise ValueError("Unsupported language: if predictor_path is None, then lang must be " +\
"'en' for English, 'zh' for Chinese, or 'ru' for Chinese")
else:
if not os.path.isfile(predictor_path) or not os.path.isfile(predictor_path +'.preproc'):
raise ValueError('could not find a valid predictor model '+\
'%s or valid Preprocessor %s at specified path' % (predictor_path, predictor_path+'.preproc'))
fpath = predictor_path
try:
import io
from contextlib import redirect_stdout
f = io.StringIO()
with redirect_stdout(f):
import ktrain
except:
raise ValueError('ktrain could not be imported. Install with: pip install ktrain')
self.predictor = ktrain.load_predictor(fpath)
def predict(self, texts, merge_tokens=True, batch_size=32):
"""
```
Extract named entities from supplied text
Args:
texts (list of str or str): list of texts to annotate
merge_tokens(bool): If True, tokens will be merged together by the entity
to which they are associated:
('Paul', 'B-PER'), ('Newman', 'I-PER') becomes ('Paul Newman', 'PER')
batch_size(int): Batch size to use for predictions (default:32)
```
"""
if isinstance(texts, str): texts = [texts]
self.predictor.batch_size = batch_size
texts = [t.strip() for t in texts]
results = self.predictor.predict(texts, merge_tokens=merge_tokens)
if len(results) == 1: results = results[0]
return results</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.shallownlp.NER.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, texts, merge_tokens=True, batch_size=32)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Extract named entities from supplied text
Args:
texts (list of str or str): list of texts to annotate
merge_tokens(bool): If True, tokens will be merged together by the entity