# Automl_tools: automl binary classification
[](https://opensource.org/licenses/Apache-2.0)
[](pyup)
[](pyup)
[](codecov)
Automl_tools is a Python library that implements Gradient Boosting
## Installation
The code is packaged for PyPI, so that the installation consists in running:
```sh
pip install automl-tools
```
## Colab
[](https://colab.research.google.com/drive/10DFkSmzMO1GqLX-mgBWfDjS9OIVmEy6O?usp=sharing)
## Usage
Probabilistic binary example on the Boston housing dataset:
```python
import pandas as pd
from automl_tools import automl_run
train = pd.read_csv("https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/train.csv?token=AAN2ZBDWF77QITK4ARSFIFDABUGAU")
test = pd.read_csv("https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/test.csv?token=AAN2ZBD6TMUC5XSGRTJNVPDABUGCO")
automl_run(train=train,
test=test,
id_col=None,
target_col="Survived",
imp_num="knn",
imp_cat="knn",
processing="binding",
mutual_information=False,
correlation_drop=False,
model_feature_selection=None,
model_run="LR",
augmentation=True,
Stratified=True,
cv=5)
```
## Parameter
```sh
imp_num : "gaussian", "arbitrary", "median", "mean", "random", "knn"
imp_cat : "frequent", "constant", "rare", "knn"
processing: "woe", "binding"
```
## Support Binary
```sh
model_feature_selection:
default: ["LR", "RF", "LGB"]
LR : LogisticRegression
RF : RandomForestClassifier
SVM : SVC
LS : LASSO
RD : RIDGE
NET : Elasticnet
DT : DecisionTreeClassifier
ET : ExtraTreesClassifier
GB : GradientBoostingClassifier
AB : AdaBoostClassifier
XGB : XGBClassifier
LGB : LGBMClassifier
CTB : CatBoostClassifier
NGB : NGBClassifier
model_run:
default: "LR"
LR : LogisticRegression
RF : RandomForestClassifier
SVM : SVC
LS : LASSO
RD : RIDGE
NET : Elasticnet
DT : DecisionTreeClassifier
ET : ExtraTreesClassifier
GB : GradientBoostingClassifier
AB : AdaBoostClassifier
XGB : XGBClassifier
LGB : LGBMClassifier
CTB : CatBoostClassifier
NGB : NGBClassifier
```
## License
[Apache License 2.0](https://www.dropbox.com/s/8t6xtgk06o3ij61/LICENSE?dl=0).
## New features v1.0
* multi_class
* regression
* integrations GCP deploy model CI/CD
* integrations AWS deploy model CI/CD
## BugFix
- 0.1.5
- fix imputer
- fix space hyperparameter
- update catboost test
- 0.1.4
- add parameter cv
- add confusion Matrix
- add comments readme.txt
- 0.1.3
- add parameter id_col
- add comments readme.txt
## Reference
- Jonathan Quiza [github](https://github.com/jonaqp).
- Jonathan Quiza [RumiMLSpark](http://rumi-ml.herokuapp.com/).
- Jonathan Quiza [linkedin](https://www.linkedin.com/in/jonaqp/).
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"description": "# Automl_tools: automl binary classification\r\n\r\n\r\n[](https://opensource.org/licenses/Apache-2.0)\r\n[](pyup)\r\n[](pyup)\r\n[](codecov)\r\n\r\n\r\n\r\n\r\nAutoml_tools is a Python library that implements Gradient Boosting\r\n## Installation\r\n\r\nThe code is packaged for PyPI, so that the installation consists in running:\r\n```sh\r\npip install automl-tools\r\n```\r\n\r\n## Colab\r\n\r\n[](https://colab.research.google.com/drive/10DFkSmzMO1GqLX-mgBWfDjS9OIVmEy6O?usp=sharing)\r\n\r\n\r\n## Usage\r\n\r\nProbabilistic binary example on the Boston housing dataset:\r\n\r\n```python\r\nimport pandas as pd\r\nfrom automl_tools import automl_run\r\n\r\ntrain = pd.read_csv(\"https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/train.csv?token=AAN2ZBDWF77QITK4ARSFIFDABUGAU\")\r\ntest = pd.read_csv(\"https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/test.csv?token=AAN2ZBD6TMUC5XSGRTJNVPDABUGCO\")\r\n\r\nautoml_run(train=train,\r\n test=test,\r\n id_col=None, \r\n target_col=\"Survived\",\r\n imp_num=\"knn\",\r\n imp_cat=\"knn\",\r\n processing=\"binding\",\r\n mutual_information=False,\r\n correlation_drop=False,\r\n model_feature_selection=None,\r\n model_run=\"LR\",\r\n augmentation=True,\r\n Stratified=True,\r\n cv=5)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n```\r\n\r\n## Parameter\r\n```sh\r\nimp_num : \"gaussian\", \"arbitrary\", \"median\", \"mean\", \"random\", \"knn\"\r\nimp_cat : \"frequent\", \"constant\", \"rare\", \"knn\"\r\nprocessing: \"woe\", \"binding\" \r\n```\r\n\r\n## Support Binary\r\n```sh\r\nmodel_feature_selection: \r\n default: [\"LR\", \"RF\", \"LGB\"]\r\n LR : LogisticRegression\r\n RF : RandomForestClassifier\r\n SVM : SVC\r\n LS : LASSO\r\n RD : RIDGE\r\n NET : Elasticnet\r\n DT : DecisionTreeClassifier\r\n ET : ExtraTreesClassifier\r\n GB : GradientBoostingClassifier\r\n AB : AdaBoostClassifier\r\n XGB : XGBClassifier\r\n LGB : LGBMClassifier\r\n CTB : CatBoostClassifier\r\n NGB : NGBClassifier\r\n\r\nmodel_run:\r\n default: \"LR\"\r\n LR : LogisticRegression\r\n RF : RandomForestClassifier\r\n SVM : SVC\r\n LS : LASSO\r\n RD : RIDGE\r\n NET : Elasticnet\r\n DT : DecisionTreeClassifier\r\n ET : ExtraTreesClassifier\r\n GB : GradientBoostingClassifier\r\n AB : AdaBoostClassifier\r\n XGB : XGBClassifier\r\n LGB : LGBMClassifier\r\n CTB : CatBoostClassifier\r\n NGB : NGBClassifier\r\n```\r\n\r\n## License\r\n\r\n[Apache License 2.0](https://www.dropbox.com/s/8t6xtgk06o3ij61/LICENSE?dl=0).\r\n\r\n\r\n## New features v1.0\r\n * multi_class\r\n * regression\r\n * integrations GCP deploy model CI/CD\r\n * integrations AWS deploy model CI/CD\r\n \r\n## BugFix\r\n - 0.1.5\r\n - fix imputer\r\n - fix space hyperparameter\r\n - update catboost test\r\n \r\n - 0.1.4\r\n - add parameter cv\r\n - add confusion Matrix\r\n - add comments readme.txt\r\n \r\n - 0.1.3\r\n - add parameter id_col\r\n - add comments readme.txt\r\n\r\n\r\n\r\n## Reference\r\n\r\n - Jonathan Quiza [github](https://github.com/jonaqp).\r\n - Jonathan Quiza [RumiMLSpark](http://rumi-ml.herokuapp.com/).\r\n - Jonathan Quiza [linkedin](https://www.linkedin.com/in/jonaqp/).\r\n\r\n\r\n\r\n",
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