nlpbasekit 0.1.3

An easy-to-use library with advanced preprocessing features to streamline and accelerate machine learning workflows.

NLPkit

A toolkit that wraps popular scikit-learn models with NLP-aware preprocessing and useful utilities. NLPkit simplifies text classification and clustering by offering:

• Tokenization
• Stop-word removal
• Contraction expansion
• Punctuation stripping
• Rare word filtering
• Vectorization
• Automated Model management & Evaluation

Table of Contents

1. Installation
2. Quick Start
3. Features
4. LogisticClassifier API
   • Initialization Parameters
   • Model Methods
5. SupportVectorClassifier API
6. DTC API
7. NeigbhorClassifier API
8. SGD API
9. GBC API
10. GaussianProcess API
11. NMeans API

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Installation

Install via pip:

pip install nlpkit

---

Quick Start

from nlpkit import LogisticClassifier

# Sample data
docs = [
    "The food was absolutely fantastic!",
    "I can't stand the traffic here.",
    "What a beautiful experience that was.",
    "This product broke after one use.",
    "Totally worth the price!",
    "I wouldn't recommend this to anyone.",
    "Had a great time with my friends.",
    "The customer service was disappointing.",
    "Everything about this place was perfect.",
    "It was a complete waste of money."
]

labels = [
    "positive","negative","positive","negative","positive",
    "negative","positive","negative","positive","negative"]
# Initialize and train the classifier
clf = LogisticClassifier(
    embedding='tfidf',       # 'count' or 'tfidf'
    n=1,                     # 1 = unigram
    stop='english',          # language for stop words
    punc=True,               # remove punctuation
    normalization_method='lemmatization',  # or 'stemming'
    rare_words=0.01          # drop words in bottom 1%
)
clf.fit(docs, labels)

# Predict and evaluate
print(clf.predict(["What a wonderful experience?"]))
print(clf.getClassificationReport())

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Features

• Text Preprocessing
  • Tokenization
  • Stop-word removal
  • Contraction expansion (e.g., "don't" → "do not")
  • Punctuation removal
  • Rare word filtering by proportion or count
  • Normalization: stemming or lemmatization
• Vectorization
  • Bag-of-words (CountVectorizer)
  • TF-IDF (TfidfVectorizer)
• Model Management
  • fit, predict, predict_proba, score
  • Export trained pipeline to file
  • Inspect coefficients and intercept
• Evaluation
  • Confusion matrix
  • Classification report
  • Precision, recall, and F1 scores

---

LogisticClassifier API

Initialization Parameters

Parameter	Type	Default	Description
penalty	str	'l2'	Regularization penalty (see LogisticRegression).
dual	bool	False	Dual or primal formulation.
tol	float	1e-4	Tolerance for stopping criteria.
C	float	1.0	Inverse of regularization strength.
All other sklearn.linear_model LogisticRegression args			Supported (e.g., solver, max_iter).
NLP-specific
embedding	str	'count'	'count' or 'tfidf'.
n	int	1	N-gram size (1 = unigram, 2 = bigram, etc.).
stop	str	'english'	Stop-word language for NLTK.
punc	bool	True	Remove punctuation if True.
extraction	bool	True	Expand contractions if True.
rare_words	float or int	0	Proportion or count threshold to drop rare words.
normalization_method	str or None	None	'stemming', 'lemmatization', or None.

Model Methods

fit(X: List[str], y: List[Any]) -> None

Train on raw text and labels.

predict(X: List[str]) -> np.ndarray

Predict class labels.

predict_proba(X: List[str]) -> np.ndarray

Return class probabilities.

score(X: List[str], y: List[Any]) -> float

Mean accuracy on test data.

export_model(path: str, model_name: str) -> None

Save pipeline as <path>/<model_name>.pkl.

getCoefficients() -> np.ndarray

Return learned feature coefficients.

getIntercept() -> np.ndarray

Return model intercept.

getConfusionMatrix() -> np.ndarray

Compute confusion matrix on the last predictions.

getClassificationReport() -> str

Detailed precision, recall, F1 by class.

getPrecisionScore() -> float

Precision score (binary or macro-averaged).

---

SupportVectorClassifier API

All parameters in the first section are forwarded directly to sklearn.svm.SVC and behave exactly as in scikit-learn’s documentation.

• NLP-specific parameters (same as in the LogisticClassifier API):
  • embedding
  • n
  • stop
  • punc
  • extraction
  • rare_words
  • normalization_method

After fitting, you can call the following methods:

predict(X: List[str]) -> np.ndarray
predict_proba(X: List[str]) -> np.ndarray
score(X: List[str], y: List[Any]) -> float
getCoefficients() -> np.ndarray
getIntercept() -> np.ndarray
getConfusionMatrix() -> np.ndarray
getClassificationReport() -> str
getPrecisionScore() -> float
export_model(path: str, model_name: str) -> None

---

DTC API

All parameters in the first section are forwarded directly to sklearn.tree DecisionTreeClassifier and behave exactly as in scikit-learn’s documentation.

• NLP-specific parameters (same as in the LogisticClassifier API):
  • embedding
  • n
  • stop
  • punc
  • extraction
  • rare_words
  • normalization_method

After fitting, you can call the following methods:

predict(X: List[str]) -> np.ndarray
predict_proba(X: List[str]) -> np.ndarray
score(X: List[str], y: List[Any]) -> float
getCoefficients() -> np.ndarray
getIntercept() -> np.ndarray
getConfusionMatrix() -> np.ndarray
getClassificationReport() -> str
getPrecisionScore() -> float
export_model(path: str, model_name: str) -> None

---

NeigbhorClassifier API

All parameters in the first section are forwarded directly to sklearn.neighbors KNeighborsClassifier and behave exactly as in scikit-learn’s documentation.

• NLP-specific parameters (same as in the LogisticClassifier API):
  • embedding
  • n
  • stop
  • punc
  • extraction
  • rare_words
  • normalization_method

After fitting, you can call the following methods:

predict(X: List[str]) -> np.ndarray
predict_proba(X: List[str]) -> np.ndarray
score(X: List[str], y: List[Any]) -> float
getCoefficients() -> np.ndarray
getIntercept() -> np.ndarray
getConfusionMatrix() -> np.ndarray
getClassificationReport() -> str
getPrecisionScore() -> float
export_model(path: str, model_name: str) -> None

---

SGD API

All parameters in the first section are forwarded directly to sklearn.linear_model SGDClassifier and behave exactly as in scikit-learn’s documentation.

• NLP-specific parameters (same as in the LogisticClassifier API):
  • embedding
  • n
  • stop
  • punc
  • extraction
  • rare_words
  • normalization_method

After fitting, you can call the following methods:

predict(X: List[str]) -> np.ndarray
predict_proba(X: List[str]) -> np.ndarray
score(X: List[str], y: List[Any]) -> float
getCoefficients() -> np.ndarray
getIntercept() -> np.ndarray
getConfusionMatrix() -> np.ndarray
getClassificationReport() -> str
getPrecisionScore() -> float
export_model(path: str, model_name: str) -> None

---

GBC API

All parameters in the first section are forwarded directly to sklearn.ensemble GradientBoostingClassifier and behave exactly as in scikit-learn’s documentation.

• NLP-specific parameters (same as in the LogisticClassifier API):
  • embedding
  • n
  • stop
  • punc
  • extraction
  • rare_words
  • normalization_method

After fitting, you can call the following methods:

predict(X: List[str]) -> np.ndarray
predict_proba(X: List[str]) -> np.ndarray
score(X: List[str], y: List[Any]) -> float
getCoefficients() -> np.ndarray
getIntercept() -> np.ndarray
getConfusionMatrix() -> np.ndarray
getClassificationReport() -> str
getPrecisionScore() -> float
export_model(path: str, model_name: str) -> None

---

GaussianProcess API

All parameters in the first section are forwarded directly to sklearn.gaussian_process GaussianProcessClassifier and behave exactly as in scikit-learn’s documentation.

• NLP-specific parameters (same as in the LogisticClassifier API):
  • embedding
  • n
  • stop
  • punc
  • extraction
  • rare_words
  • normalization_method

After fitting, you can call the following methods:

predict(X: List[str]) -> np.ndarray
predict_proba(X: List[str]) -> np.ndarray
score(X: List[str], y: List[Any]) -> float
getCoefficients() -> np.ndarray
getIntercept() -> np.ndarray
getConfusionMatrix() -> np.ndarray
getClassificationReport() -> str
getPrecisionScore() -> float
export_model(path: str, model_name: str) -> None

---

NMeans API

All parameters in the first section are forwarded directly to sklearn.cluster KMeans and behave exactly as in scikit-learn’s documentation.

• NLP-specific parameters (same as in the LogisticClassifier API):
  • embedding
  • n
  • stop
  • punc
  • extraction
  • rare_words
  • normalization_method

Model Methods

fit(X: array-like) -> None

Preprocess texts and fit KMeans on the feature matrix.

predict(X: array-like) -> np.ndarray

Preprocess and predict cluster assignments.

export_model(path: str, model_name: str) -> None

Save the fitted KMeans model as a .joblib file at <path>/<model_name>.joblib.

get_centroids() -> np.ndarray

Return cluster centroids.

get_labels() -> np.ndarray

Return labels assigned to each sample.

get_inertia() -> float

Return the final inertia (sum of squared distances to nearest centroid).

get_n_iterations() -> int

Return the number of iterations run.

get_n_features() -> int

Return the number of features seen during fit.

get_groups() -> Dict[str, List[str]]

Return a dict mapping each cluster label to the list of original inputs assigned to that cluster.

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