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feature-selection-lofo-0.0.6
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
This is an implementation of LOFO for automatic feature selection.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | feature-selection-lofo-0.0.6 |
| نام | feature-selection-lofo |
| نسخه کتابخانه | 0.0.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Azzam Radman <azzamradman1993@gmail.com> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/feature-selection-lofo/ |
| مجوز | MIT License Copyright (c) 2022 Azzam-Radman Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
# LOFO
Leave One Feature Out (**LOFO**) is on of the most powerful techniques for feature selection.
This repository contains the implementation of **LOFO** in Python and can be used with any model of the followings:
1. Any Scikit-Learn model.
2. Any TensorFlow/Keras model.
3. LightGBM.
4. CatBoost.
5. XGBoost.
# Usage
- Install the package:
```
pip install feature-selection-lofo
```
- Import lofo
```
from feature_selection_lofo import lofo
```
```
lofo.LOFO(X, Y,
model,
cv,
metric,
direction,
fit_params=None,
predict_type='predict',
return_bad_feats=False,
groups=None,
is_keras_model=False)
```
|Args||
|---|---|
|X| Pandas DataFrame, input features to the model (predictors).|
|Y| array_like, target/label feature.|
|model| object, the model class (e.g. sklearn.linear_model.LinearRegression()).|
|cv| object, sklearn cross validatoin object (e.g. sklearn.model_selection.KFold(n_splits=5, shuffle=True, random_state=0)).|
|metric| object, metric to use during search (e.g. sklearn.metrics.roc_auc_score).|
|direction| string, direction of optimization ('max' or 'min').|
|fit_params| string, parameters to use for fitting (e.g. "{'X': x_train, 'y': y_train}") . Defaults to "{'X': x_train, 'y': y_train}".|
|predict_type| string, ('predict' or 'predict_proba'). Defaults to 'predict'.|
|return_bad_feats| boolean, whether to return a list of bad features. Defaults to False.|
|groups| array_like, used with StratifiedGroupKFold. Defaults to None.|
|is_keras_model| boolean, whether the model passed is Keras model. Defaults to False.|
|Returns|
|---|
|A Pandas DataFrame with harmful features removed.|
|If return_bad_feats is set to True, it returns a list of the harmful features.|
- Import the needed libraries for your model, cross-validation, etc
### Scikit-Learn Model Example
```
import warnings
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
```
- Define the paramters
```
# shutdown warning messages
warnings.filterwarnings('ignore')
X = train_df.iloc[:, :-1]
Y = train_df.iloc[:, -1]
model = LogisticRegression()
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
metric = roc_auc_score
direction = 'max'
fit_params = "{'X': x_train, 'y': y_train}"
predict_type = 'predict_proba'
return_bad_feats = True
groups = None
is_keras_model = False
```
- Define the LOFO object and call it
```
lofo_object = lofo.LOFO(X, Y, model, cv, metric, direction, fit_params,
predict_type, return_bad_feats, groups, is_keras_model)
clean_X, bad_feats = lofo_object()
```
clean_X: is the dataset containing the useful features only.
bad_feats: are the harmful or useless features.
### LightGBM Model Example
```
import warnings
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
import lightgbm as lgbm
```
- Define the paramters
```
# shutdown warning messages
warnings.filterwarnings('ignore')
X = train_df.iloc[:, :-1]
Y = train_df.iloc[:, -1]
model= lgbm.LGBMClassifier(
objective='binary',
metric='auc',
subsample=0.7,
learning_rate=0.03,
n_estimators=100,
n_jobs=-1)
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
metric = roc_auc_score
direction = 'max'
fit_params = "{'X': x_train, 'y': y_train, 'eval_set': [(x_valid,y_valid)], 'verbose': 0}"
predict_type = 'predict_proba'
return_bad_feats = True
groups = None
is_keras_model = False
```
- Define the LOFO object and call it
```
lofo_object = lofo.LOFO(X, Y, model, cv, metric, direction, fit_params,
predict_type, return_bad_feats, groups, is_keras_model)
clean_X, bad_feats = lofo_object()
```
### TensorFlow/Keras Model Example
```
import warnings
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
import tensorflow as tf
from tensorflow.keras import layers
```
- Construct the model
```
def nn_model():
inputs = layers.Input(shape=X.shape[-1],)
x = layers.Dense(256, activation='relu')(inputs)
x = layers.Dense(64, activation='relu')(x)
output = layers.Dense(1, activation='sigmoid')(x)
model = tf.keras.Model(inputs=inputs, outputs=output)
model.compile(loss='binary_crossentropy',
optimizer='adam',)
return model
```
- Define the paramters
```
# shutdown warning messages
warnings.filterwarnings('ignore')
X = train_df.iloc[:, :-1]
Y = train_df.iloc[:, -1]
tf.keras.backend.clear_session()
model = nn_model()
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
metric = roc_auc_score
direction = 'max'
fit_params = "{'x': x_train, 'y': y_train, 'validation_data': (x_valid, y_valid), 'epochs': 10, 'batch_size': 256, 'verbose': 0}"
predict_type = 'predict'
return_bad_feats = True
groups = None
is_keras_model = True
```
- Define the LOFO object and call it
```
lofo_object = lofo.LOFO(X, Y, model, cv, metric, direction, fit_params,
predict_type, return_bad_feats, groups, is_keras_model)
clean_X, bad_feats = lofo_object()
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl feature-selection-lofo-0.0.6:
pip install feature-selection-lofo-0.0.6.whl
نصب پکیج tar.gz feature-selection-lofo-0.0.6:
pip install feature-selection-lofo-0.0.6.tar.gz