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https://github.com/dayyass/text-classification-baseline
Pipeline for fast building text classification TF-IDF + LogReg baselines.
https://github.com/dayyass/text-classification-baseline
baseline classification data-science fast hacktoberfest logistic-regression machine-learning natural-language-processing nlp python text text-classification tf-idf
Last synced: 3 months ago
JSON representation
Pipeline for fast building text classification TF-IDF + LogReg baselines.
- Host: GitHub
- URL: https://github.com/dayyass/text-classification-baseline
- Owner: dayyass
- License: mit
- Created: 2021-07-27T19:11:31.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-11-06T09:00:24.000Z (about 3 years ago)
- Last Synced: 2024-09-14T07:47:25.628Z (5 months ago)
- Topics: baseline, classification, data-science, fast, hacktoberfest, logistic-regression, machine-learning, natural-language-processing, nlp, python, text, text-classification, tf-idf
- Language: Python
- Homepage: https://pypi.org/project/text-classification-baseline/
- Size: 1.56 MB
- Stars: 63
- Watchers: 2
- Forks: 4
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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## Text Classification Baseline
Pipeline for fast building text classification baselines with **TF-IDF + LogReg**.
## Usage
Instead of writing custom code for specific text classification task, you just need:
1. install pipeline:
```shell script
pip install text-classification-baseline
```
2. run pipeline:
- either in **terminal**:
```shell script
text-clf-train --path_to_config config.yaml
```
- or in **python**:
```python3
import text_clf
model, target_names_mapping = text_clf.train(path_to_config="config.yaml")
```
**NOTE**: more about config file [here](https://github.com/dayyass/text-classification-baseline/tree/main#config).
No data preparation is needed, only a **csv** file with two raw columns (with arbitrary names):
- `text`
- `target`
The **target** can be presented in any format, including text - not necessarily integers from *0* to *n_classes-1*.
### Config
The user interface consists of two files:
- [**config.yaml**](https://github.com/dayyass/text-classification-baseline/blob/main/config.yaml) - general configuration with sklearn **TF-IDF** and **LogReg** parameters
- [**hyperparams.py**](https://github.com/dayyass/text-classification-baseline/blob/main/hyperparams.py) - sklearn **GridSearchCV** parameters
Change **config.yaml** and **hyperparams.py** to create the desired configuration and train text classification model with the following command:
- **terminal**:
```shell script
text-clf-train --path_to_config config.yaml
```
- **python**:
```python3
import text_clf
model, target_names_mapping = text_clf.train(path_to_config="config.yaml")
```
Default **config.yaml**:
```yaml
seed: 42
path_to_save_folder: models
experiment_name: model
# data
data:
train_data_path: data/train.csv
test_data_path: data/test.csv
sep: ','
text_column: text
target_column: target_name_short
# preprocessing
# (included in resulting model pipeline, so preserved for inference)
preprocessing:
lemmatization: null # pymorphy2
# tf-idf
tf-idf:
lowercase: true
ngram_range: (1, 1)
max_df: 1.0
min_df: 1
# logreg
logreg:
penalty: l2
C: 1.0
class_weight: balanced
solver: saga
n_jobs: -1
# grid-search
grid-search:
do_grid_search: false
grid_search_params_path: hyperparams.py
```
**NOTE**: grid search is disabled by default, to use it set `do_grid_search: true`.
**NOTE**: `tf-idf` and `logreg` are sklearn [**TfidfVectorizer**](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html?highlight=tfidf#sklearn.feature_extraction.text.TfidfVectorizer) and [**LogisticRegression**](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) parameters correspondingly, so you can parameterize instances of these classes however you want. The same logic applies to `grid-search` which is sklearn [**GridSearchCV**](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV) parametrized with [**hyperparams.py**](https://github.com/dayyass/text-classification-baseline/blob/main/hyperparams.py).
### Output
After training the model, the pipeline will return the following files:
- `model.joblib` - sklearn pipeline with TF-IDF and LogReg steps
- `target_names.json` - mapping from encoded target labels from *0* to *n_classes-1* to it names
- `config.yaml` - config that was used to train the model
- `hyperparams.py` - grid-search parameters (if grid-search was used)
- `logging.txt` - logging file
### Additional functions
- `text_clf.token_frequency.get_token_frequency(path_to_config)` -
get token frequency of **train dataset** according to the config file parameters
**Only for binary classifiers**:
- `text_clf.pr_roc_curve.get_precision_recall_curve(path_to_model_folder)` -
get *precision* and *recall* metrics for precision-recall curve
- `text_clf.pr_roc_curve.get_roc_curve(path_to_model_folder)` -
get *false positive rate (fpr)* and *true positive rate (tpr)* metrics for roc curve
- `text_clf.pr_roc_curve.plot_precision_recall_curve(precision, recall)` -
plot *precision-recall curve*
- `text_clf.pr_roc_curve.plot_roc_curve(fpr, tpr)` -
plot *roc curve*
- `text_clf.pr_roc_curve.plot_precision_recall_f1_curves_for_thresholds(precision, recall, thresholds)` -
plot *precision*, *recall*, *f1-score* curves for probability thresholds
## Requirements
Python >= 3.6
## Citation
If you use **text-classification-baseline** in a scientific publication, we would appreciate references to the following BibTex entry:
```bibtex
@misc{dayyass2021textclf,
author = {El-Ayyass, Dani},
title = {Pipeline for training text classification baselines},
howpublished = {\url{https://github.com/dayyass/text-classification-baseline}},
year = {2021}
}
```