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<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text.shallownlp.classifier</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from . import utils as U
from .imports import *
__all__ = ["NBSVM"]
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, use_tfidf=False, **kwargs):
"""
```
create a model
Args:
ctype(str): one of {'nbsvm', 'logreg', 'sgdclassifier'}
texts(list): list of texts
kwargs(dict): additional parameters should have one of the following prefixes:
vec__ : hyperparameters to CountVectorizer (e.g., vec__max_features=10000)
tfidf__ : hyperparameters to TfidfTransformer
clf__: hyperparameters to classifier (specific to ctype).
If ctype='logreg', then an example is clf__solver='liblinear'.
```
"""
if ctype == "nbsvm":
if kwargs.get("vec__binary", False) is False:
warnings.warn("nbsvm must use binary=True - changing automatically")
if use_tfidf:
warnings.warn("nbsvm must use use_tfidf=False = changing automatically")
vec_kwargs = dict(
(k.replace("vec__", ""), kwargs[k]) for k in kwargs if k.startswith("vec__")
)
tfidf_kwargs = dict(
(k.replace("tfidf__", ""), kwargs[k])
for k in kwargs
if k.startswith("tfidf__")
)
clf_kwargs = dict(
(k.replace("clf__", ""), kwargs[k]) for k in kwargs if k.startswith("clf__")
)
lang = U.detect_lang(texts)
if U.is_chinese(lang) and not vec_kwargs.get("token_pattern", None):
vec_kwargs["token_pattern"] = r"(?u)\b\w+\b"
elif not kwargs.get("vec__token_pattern", None):
vec_kwargs["token_pattern"] = r"\w+|[%s]" % string.punctuation
if ctype == "nbsvm":
clf = NBSVM(**clf_kwargs)
elif ctype == "logreg":
clf = LogisticRegression(**clf_kwargs)
elif ctype == "sgdclassifier":
clf = SGDClassifier(**clf_kwargs)
else:
raise ValueError("Unknown ctype: %s" % (ctype))
pipeline = [("vect", CountVectorizer(**vec_kwargs))]
if use_tfidf:
pipeline.append(("tfidf", TfidfTransformer(**tfidf_kwargs)))
pipeline.append(("clf", clf))
self.model = Pipeline(pipeline)
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
class NBSVM(BaseEstimator, LinearClassifierMixin, SparseCoefMixin):
def __init__(self, alpha=0.75, C=0.01, beta=0.25, fit_intercept=False):
self.alpha = alpha
self.C = C
self.beta = beta
self.fit_intercept = fit_intercept
def fit(self, X, y):
self.classes_ = np.unique(y)
if len(self.classes_) == 2:
coef_, intercept_ = self._fit_binary(X, y)
self.coef_ = coef_
self.intercept_ = intercept_
else:
coef_, intercept_ = zip(
*[self._fit_binary(X, y == class_) for class_ in self.classes_]
)
self.coef_ = np.concatenate(coef_)
self.intercept_ = np.array(intercept_).flatten()
return self
def _fit_binary(self, X, y):
p = np.asarray(self.alpha + X[y == 1].sum(axis=0)).flatten()
q = np.asarray(self.alpha + X[y == 0].sum(axis=0)).flatten()
r = np.log(p / np.abs(p).sum()) - np.log(q / np.abs(q).sum())
b = np.log((y == 1).sum()) - np.log((y == 0).sum())
if isinstance(X, spmatrix):
indices = np.arange(len(r))
r_sparse = coo_matrix((r, (indices, indices)), shape=(len(r), len(r)))
X_scaled = X * r_sparse
else:
X_scaled = X * r
lsvc = LinearSVC(
C=self.C, fit_intercept=self.fit_intercept, max_iter=10000, dual=True
).fit(X_scaled, y)
mean_mag = np.abs(lsvc.coef_).mean()
coef_ = (1 - self.beta) * mean_mag * r + self.beta * (r * lsvc.coef_)
intercept_ = (1 - self.beta) * mean_mag * b + self.beta * lsvc.intercept_
return coef_, intercept_</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.shallownlp.classifier.NBSVM"><code class="flex name class">
<span>class <span class="ident">NBSVM</span></span>
<span>(</span><span>alpha=0.75, C=0.01, beta=0.25, fit_intercept=False)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all estimators in scikit-learn.</p>
<h2 id="notes">Notes</h2>
<p>All estimators should specify all the parameters that can be set
at the class level in their <code>__init__</code> as explicit keyword
arguments (no <code>*args</code> or <code>**kwargs</code>).</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class NBSVM(BaseEstimator, LinearClassifierMixin, SparseCoefMixin):
def __init__(self, alpha=0.75, C=0.01, beta=0.25, fit_intercept=False):
self.alpha = alpha
self.C = C
self.beta = beta
self.fit_intercept = fit_intercept
def fit(self, X, y):
self.classes_ = np.unique(y)
if len(self.classes_) == 2:
coef_, intercept_ = self._fit_binary(X, y)
self.coef_ = coef_
self.intercept_ = intercept_
else:
coef_, intercept_ = zip(
*[self._fit_binary(X, y == class_) for class_ in self.classes_]
)
self.coef_ = np.concatenate(coef_)
self.intercept_ = np.array(intercept_).flatten()
return self
def _fit_binary(self, X, y):
p = np.asarray(self.alpha + X[y == 1].sum(axis=0)).flatten()
q = np.asarray(self.alpha + X[y == 0].sum(axis=0)).flatten()
r = np.log(p / np.abs(p).sum()) - np.log(q / np.abs(q).sum())
b = np.log((y == 1).sum()) - np.log((y == 0).sum())
if isinstance(X, spmatrix):
indices = np.arange(len(r))
r_sparse = coo_matrix((r, (indices, indices)), shape=(len(r), len(r)))
X_scaled = X * r_sparse
else:
X_scaled = X * r
lsvc = LinearSVC(
C=self.C, fit_intercept=self.fit_intercept, max_iter=10000, dual=True
).fit(X_scaled, y)
mean_mag = np.abs(lsvc.coef_).mean()
coef_ = (1 - self.beta) * mean_mag * r + self.beta * (r * lsvc.coef_)
intercept_ = (1 - self.beta) * mean_mag * b + self.beta * lsvc.intercept_
return coef_, intercept_</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>sklearn.base.BaseEstimator</li>
<li>sklearn.utils._metadata_requests._MetadataRequester</li>
<li>sklearn.linear_model._base.LinearClassifierMixin</li>
<li>sklearn.base.ClassifierMixin</li>
<li>sklearn.linear_model._base.SparseCoefMixin</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.shallownlp.classifier.NBSVM.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, X, y)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, X, y):
self.classes_ = np.unique(y)
if len(self.classes_) == 2:
coef_, intercept_ = self._fit_binary(X, y)
self.coef_ = coef_
self.intercept_ = intercept_
else:
coef_, intercept_ = zip(
*[self._fit_binary(X, y == class_) for class_ in self.classes_]
)
self.coef_ = np.concatenate(coef_)
self.intercept_ = np.array(intercept_).flatten()
return self</code></pre>
</details>
</dd>
<dt id="ktrain.text.shallownlp.classifier.NBSVM.set_score_request"><code class="name flex">
<span>def <span class="ident">set_score_request</span></span>(<span>self: <a title="ktrain.text.shallownlp.classifier.NBSVM" href="#ktrain.text.shallownlp.classifier.NBSVM">NBSVM</a>, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> <a title="ktrain.text.shallownlp.classifier.NBSVM" href="#ktrain.text.shallownlp.classifier.NBSVM">NBSVM</a></span>
</code></dt>
<dd>
<div class="desc"><p>Request metadata passed to the <code>score</code> method.</p>
<p>Note that this method is only relevant if
<code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>).
Please see :ref:<code>User Guide <metadata_routing></code> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul>
<li>
<p><code>True</code>: metadata is requested, and passed to <code>score</code> if provided. The request is ignored if metadata is not provided.</p>
</li>
<li>
<p><code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>score</code>.</p>
</li>
<li>
<p><code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p>
</li>
<li>
<p><code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p>
</li>
</ul>
<p>The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="admonition versionadded">
<p class="admonition-title">Added in version: 1.3</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
:class:<code>pipeline.Pipeline</code>. Otherwise it has no effect.</p>
</div>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>sample_weight</code></strong> : <code>str, True, False,</code> or <code>None</code>,
default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
<dd>Metadata routing for <code>sample_weight</code> parameter in <code>score</code>.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>self</code></strong> : <code>object</code></dt>
<dd>The updated object.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_enabled():
raise RuntimeError(
"This method is only available when metadata routing is enabled."
" You can enable it using"
" sklearn.set_config(enable_metadata_routing=True)."
)
if self.validate_keys and (set(kw) - set(self.keys)):
raise TypeError(
f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
f" are: {set(self.keys)}"
)
requests = instance._get_metadata_request()
method_metadata_request = getattr(requests, self.name)
for prop, alias in kw.items():
if alias is not UNCHANGED:
method_metadata_request.add_request(param=prop, alias=alias)
instance._metadata_request = requests
return instance</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.text.shallownlp" href="index.html">ktrain.text.shallownlp</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.text.shallownlp.classifier.NBSVM" href="#ktrain.text.shallownlp.classifier.NBSVM">NBSVM</a></code></h4>
<ul class="">
<li><code><a title="ktrain.text.shallownlp.classifier.NBSVM.fit" href="#ktrain.text.shallownlp.classifier.NBSVM.fit">fit</a></code></li>
<li><code><a title="ktrain.text.shallownlp.classifier.NBSVM.set_score_request" href="#ktrain.text.shallownlp.classifier.NBSVM.set_score_request">set_score_request</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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