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feature-stuff-0.0.dev6
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مشاهده بیشترتوضیحات
Feature extraction, processing and interpretation algorithms and functions for machine learning and data science.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | feature-stuff-0.0.dev6 |
| نام | feature-stuff |
| نسخه کتابخانه | 0.0.dev6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Mihaela Mares |
| ایمیل نویسنده | mihaela.andreea.mares@gmail.com |
| آدرس صفحه اصلی | https://github.com/hiflyin/Advanced-Feature-Stuff-Lib |
| آدرس اینترنتی | https://pypi.org/project/feature-stuff/ |
| مجوز | - |
-----------------
# feature_stuff: a python machine learning library for advanced feature extraction, processing and interpretation.
<table>
<tr>
<td>Latest Release</td>
<td>
<a href="https://pypi.org/project/feature-stuff/"> see on pypi.org</a>
</td>
</tr>
<tr>
<td>Package Status</td>
<td>
<a href="https://pypi.org/project/feature-stuff/">see on pypi.org</a>
</td>
</tr>
<tr>
<td>License</td>
<td>
<a href="https://github.com/hiflyin/Feature-Stuff/blob/master/LICENSE"> see on github</a>
</td>
</tr>
<tr>
<td>Build Status</td>
<td>
<a href="https://travis-ci.org/hiflyin/Feature-Stuff/"> see on travis
</a>
</td>
</tr>
</table>
## What is it
**feature_stuff** is a Python package providing fast and flexible algorithms and functions
for extracting, processing and interpreting features:
**Numeric feature extraction**
<table>
<tr>
<td>feature_stuff.add_interactions</td>
<td>
generic function for adding interaction features to a data frame either by passing them as a list or
by passing a boosted trees model to extract the interactions from.
</td>
</tr>
<tr>
<td>feature_stuff.target_encoding</td>
<td>
target encoding of a feature column using exponential prior smoothing or mean prior smoothing
</td>
</tr>
<tr>
<td>feature_stuff.cv_target_encoding</td>
<td>
target encoding of a feature column taking cross-validation folds as input
</td>
</tr>
<tr>
<td>feature_stuff.add_knn_values</td>
<td>
creates a new feature with the K-nearest-neighbours of the values of a given feature
</td>
</tr>
<tr>
<td>feature_stuff.model_features_insights_extractions.add_group_values</td>
<td>
generic and memory efficient enrichment of features dataframe with group values
</td>
</tr>
</table>
**Model feature insights extraction**
<table>
<tr>
<td>get_xgboost_interactions</td>
<td>
takes a trained xgboost model and returns a list of interactions between features, to the order of maximum
depth of all trees.
</td>
</tr>
<tr>
</table>
## Installation
Binary installers for the latest released version are available at the [Python
package index](https://pypi.org/project/feature-stuff) .
```sh
# or PyPI
pip install feature_stuff
```
The source code is currently hosted on GitHub at:
https://github.com/hiflyin/Feature-Stuff
## Installation from sources
In the `Feature-Stuff` directory (same one where you found this file after
cloning the git repo), execute:
```sh
python setup.py install
```
or for installing in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs):
```sh
python setup.py develop
```
Alternatively, you can use `pip` if you want all the dependencies pulled
in automatically (the `-e` option is for installing it in [development
mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs)):
```sh
pip install -e .
```
## How to use it
Below are examples for some functions. See the attached API of each function/ algorithm, for a complete documentation.
# feature_stuff.add_interactions
Inputs:
df: a pandas dataframe
model: boosted trees model (currently xgboost supported only). Can be None in which case the interactions have
to be provided
interactions: list in which each element is a list of features/columns in df, default: None
Output: df containing the group values added to it
Example on extracting interactions from tree based models and adding
them as new features to your dataset.
```python
import feature_stuff as fs
import pandas as pd
import xgboost as xgb
data = pd.DataFrame({"x0":[0,1,0,1], "x1":range(4), "x2":[1,0,1,0]})
print data
x0 x1 x2
0 0 0 1
1 1 1 0
2 0 2 1
3 1 3 0
target = data.x0 * data.x1 + data.x2*data.x1
print target.tolist()
[0, 1, 2, 3]
model = xgb.train({'max_depth': 4, "seed": 123}, xgb.DMatrix(data, label=target), num_boost_round=2)
fs.addInteractions(data, model)
# at least one of the interactions in target must have been discovered by xgboost
print data
x0 x1 x2 inter_0
0 0 0 1 0
1 1 1 0 1
2 0 2 1 0
3 1 3 0 3
# if we want to inspect the interactions extracted
from feature_stuff import model_features_insights_extractions as insights
print insights.get_xgboost_interactions(model)
[['x0', 'x1']]
```
# feature_stuff.target_encoding
Inputs:
df: a pandas dataframe containing the column for which to calculate target encoding (categ_col)
ref_df: a pandas dataframe containing the column for which to calculate target encoding and the target (y_col)
for example we might want to use train data as ref_df to encode test data
categ_col: the name of the categorical column for which to calculate target encoding
y_col: the name of the target column, or target variable to predict
smoothing_func: the name of the function to be used for calculating the weights of the corresponding target
value inside ref_df. Default: exponentialPriorSmoothing.
aggr_func: the statistic used to aggregate the target variable values inside each category of the categ_col
smoothing_prior_weight: a prior weight to put on each category. Default 1.
Output: df containing a new column called <categ_col + "_bayes_" + aggr_func> containing the encodings of categ_col
Example on extracting target encodings from categorical features and adding them as new features to your dataset.
```
import feature_stuff as fs
import pandas as pd
train_data = pd.DataFrame({"x0":[0,1,0,1]})
test_data = pd.DataFrame({"x0":[1, 0, 0, 1]})
target = range(4)
train_data = fs.target_encoding(train_data, train_data, "x0", target, smoothing_func=fs.exponentialPriorSmoothing,
aggr_func="mean", smoothing_prior_weight=1)
test_data = fs.target_encoding(test_data, train_data, "x0", target, smoothing_func=fs.exponentialPriorSmoothing,
aggr_func="mean", smoothing_prior_weight=1)
#train data with target encoding of "x0"
print(train_data)
x0 y_xx g_xx x0_bayes_mean
0 0 0 0 1.134471
1 1 1 0 1.865529
2 0 2 0 1.134471
3 1 3 0 1.865529
#test data with target encoding of "x0"
print(test_data)
x0 x0_bayes_mean
0 1 1.865529
1 0 1.134471
2 0 1.134471
3 1 1.865529
```
# feature_stuff.cv_target_encoding
Inputs:
df: a pandas dataframe containing the column for which to calculate target encoding (categ_col) and the target
categ_cols: a list or array with the the names of the categorical columns for which to calculate target encoding
y_col: a numpy array of the target variable to predict
cv_folds: a list with fold pairs as tuples of numpy arrays for cross-val target encoding
smoothing_func: the name of the function to be used for calculating the weights of the corresponding target
value inside ref_df. Default: exponentialPriorSmoothing.
aggr_func: the statistic used to aggregate the target variable values inside each category of the categ_col
smoothing_prior_weight: a prior weight to put on each category. Default 1.
verbosity: 0-none, 1-high_level, 2-detailed
Output: df containing a new column called <categ_col + "_bayes_" + aggr_func> containing the encodings of categ_col
See feature_stuff.target_encoding example above.
## Contributing to feature-stuff
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.
نیازمندی
| مقدار | نام |
|---|---|
| >=0.19.2 | pandas |
| >=1.12.1 | numpy |
| >=0.18 | scikit-learn |
| >=0.19.0 | scipy |
| >=0.6 | xgboost |
| xtr | check-manifest; |
| xtr | coverage; |
نحوه نصب
نصب پکیج whl feature-stuff-0.0.dev6:
pip install feature-stuff-0.0.dev6.whl
نصب پکیج tar.gz feature-stuff-0.0.dev6:
pip install feature-stuff-0.0.dev6.tar.gz