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blitzml-0.9.0
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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
Automate machine learning pipelines rapidly
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | blitzml-0.9.0 |
| نام | blitzml |
| نسخه کتابخانه | 0.9.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | AI Team |
| ایمیل نویسنده | AI Team <CV.Team.CSE.2023@gmail.com> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/blitzml/ |
| مجوز | - |
<div align="center">
# <span style="color:violet">BlitzML</span>
### **Automate machine learning pipelines rapidly**
<div align="left">
- [Install BlitzML](#install-blitzml)
- [Classification](#classification)
- [Regression](#regression)
# Install BlitzML
```bash
pip install blitzml
```
# Classification
```python
from blitzml.tabular import Classification
import pandas as pd
# prepare your dataframes
train_df = pd.read_csv("auxiliary/datasets/banknote/train.csv")
test_df = pd.read_csv("auxiliary/datasets/banknote/test.csv")
# create the pipeline
auto = Classification(train_df, test_df, classifier = 'RF', n_estimators = 50)
# first perform data preprocessing
auto.preprocess()
# second train the model
auto.train_the_model()
# After training the model we can generate:
auto.gen_pred_df(auto.test_df)
auto.gen_metrics_dict()
# We can get their values using:
pred_df = auto.pred_df
metrics_dict = auto.metrics_dict
print(pred_df.head())
print(metrics_dict)
```
## Available Classifiers
- Random Forest 'RF'
- LinearDiscriminantAnalysis 'LDA'
- Support Vector Classifier 'SVC'
- KNeighborsClassifier 'KNN'
- GaussianNB 'GNB'
- LogisticRegression 'LR'
- AdaBoostClassifier 'AB'
- GradientBoostingClassifier 'GB'
- DecisionTreeClassifier 'DT'
- MLPClassifier 'MLP'
## **Parameters**
**classifier**
options: {'RF','LDA','SVC','KNN','GNB','LR','AB','GB','DT','MLP', 'auto', 'custom'}, default = 'RF'
`auto: selects the best scoring classifier based on f1-score`
`custom: enables providing a custom classifier through *file_path* and *class_name* parameters`
**file_path**
when using 'custom' classifier, pass the path of the file containing the custom class, default = 'none'
**class_name**
when using 'custom' classifier, pass the class name through this parameter, default = 'none'
**feature_selection**
options: {'correlation', 'importance', 'none'}, default = 'none'
`correlation: use feature columns with the highest correlation with the target`
`importance: use feature columns that are important for the model to predict the target`
`none: use all feature columns`
**validation_percentage**
value determining the validation split percentage (value from 0 to 1), default = 0.1
**average_type**
when performing multiclass classification, provide the average type for the resulting metrics, default = 'macro'
**cross_validation_k_folds**
number of k-folds for cross validation, if 1 then no cv will be performed, default = 1
****kwargs**
optional parameters for the chosen classifier. you can find available parameters in the [sklearn docs](https://scikit-learn.org/stable/user_guide.html)
## **Attributes**
**train_df**
the preprocessed train dataset (after running `Classification.preprocess()`)
**test_df**
the preprocessed test dataset (after running `Classification.preprocess()`)
**model**
the trained model (after running `Classification.train_the_model()`)
**pred_df**
the prediction dataframe (test_df + predicted target) (after running `Classification.gen_pred_df(Classification.test_df)`)
**metrics_dict**
the validation metrics (after running `Classification.gen_metrics_dict()`)
{
"accuracy": acc,
"f1": f1,
"precision": pre,
"recall": recall,
"hamming_loss": h_loss,
"cross_validation_score":cv_score, `returns None if cross_validation_k_folds==1`
}
## **Methods**
**preprocess()**
perform preprocessing on train_df and test_df
**train_the_model()**
train the chosen classifier on the train_df
**accuracy_history()**
accuracy scores when varying the sampling size of the train_df (after running `Classification.train_the_model()`).
*returns:*
{
'x':train_df_sample_sizes,
'y1':train_scores_mean,
'y2':test_scores_mean,
'title':title
}
**gen_pred_df(test_df)**
generates the prediction dataframe and assigns it to the `pred_df` attribute
**gen_metrics_dict()**
generates the validation metrics and assigns it to the `metrics_dict`
**run()**
a shortcut that runs the following methods:
preprocess()
train_the_model()
gen_pred_df(Classification.test_df)
gen_metrics_dict()
# Regression
```python
from blitzml.tabular import Regression
import pandas as pd
# prepare your dataframes
train_df = pd.read_csv("auxiliary/datasets/house prices/train.csv")
test_df = pd.read_csv("auxiliary/datasets/house prices/test.csv")
# create the pipeline
auto = Regression(train_df, test_df, regressor = 'RF')
# first perform data preprocessing
auto.preprocess()
# second train the model
auto.train_the_model()
# After training the model we can generate:
auto.gen_pred_df(auto.test_df)
auto.gen_metrics_dict()
# We can get their values using:
pred_df = auto.pred_df
metrics_dict = auto.metrics_dict
print(pred_df.head())
print(metrics_dict)
```
## Available Regressors
- Random Forest 'RF'
- Support Vector Regressor 'SVR'
- KNeighborsRegressor 'KNN'
- Lasso Regressor 'LSS'
- LinearRegression 'LR'
- Ridge Regressor 'RDG'
- GaussianProcessRegressor 'GPR'
- GradientBoostingRegressor 'GB'
- DecisionTreeRegressor 'DT'
- MLPRegressor 'MLP'
## **Parameters**
**regressor**
options: {'RF','SVR','KNN','LSS','LR','RDG','GPR','GB','DT','MLP', 'auto', 'custom'}, default = 'RF'
`auto: selects the best scoring regressor based on r2 score`
`custom: enables providing a custom regressor through *file_path* and *class_name* parameters`
**file_path**
when using 'custom' regressor, pass the path of the file containing the custom class, default = 'none'
**class_name**
when using 'custom' regressor, pass the class name through this parameter, default = 'none'
**feature_selection**
options: {'correlation', 'importance', 'none'}, default = 'none'
`correlation: use feature columns with the highest correlation with the target`
`importance: use feature columns that are important for the model to predict the target`
`none: use all feature columns`
**validation_percentage**
value determining the validation split percentage (value from 0 to 1), default = 0.1
**cross_validation_k_folds**
number of k-folds for cross validation, if 1 then no cv will be performed, default = 1
****kwargs**
optional parameters for the chosen regressor. you can find available parameters in the [sklearn docs](https://scikit-learn.org/stable/user_guide.html)
## **Attributes**
**train_df**
the preprocessed train dataset (after running `Regression.preprocess()`)
**test_df**
the preprocessed test dataset (after running `Regression.preprocess()`)
**model**
the trained model (after running `Regression.train_the_model()`)
**pred_df**
the prediction dataframe (test_df + predicted target) (after running `Regression.gen_pred_df(Regression.test_df)`)
**metrics_dict**
the validation metrics (after running `Regression.gen_metrics_dict()`)
{
"r2_score": r2,
"mean_squared_error": mse,
"root_mean_squared_error": rmse,
"mean_absolute_error" : mae,
"cross_validation_score":cv_score, `returns None if cross_validation_k_folds==1`
}
## **Methods**
**preprocess()**
perform preprocessing on train_df and test_df
**train_the_model()**
train the chosen regressor on the train_df
**RMSE_history()**
RMSE scores when varying the sampling size of the train_df (after running `Regression.train_the_model()`).
*returns:*
{
'x':train_df_sample_sizes,
'y1':train_scores_mean,
'y2':test_scores_mean,
'title':title
}
**gen_pred_df(test_df)**
generates the prediction dataframe and assigns it to the `pred_df` attribute
**gen_metrics_dict()**
generates the validation metrics and assigns it to the `metrics_dict`
**run()**
a shortcut that runs the following methods:
preprocess()
train_the_model()
gen_pred_df(Regression.test_df)
gen_metrics_dict()
## Development
- Clone the repo
- run `pip install virtualenv`
- run `python -m virtualenv venv`
- run `. ./venv/bin/activate` on UNIX based systems or `. ./venv/Scripts/activate.ps1` if on windows
- run `pip install -r requirements.txt`
- run `pre-commit install`
نیازمندی
| مقدار | نام |
|---|---|
| >=1.2.0 | joblib |
| <=1.23.4 | numpy |
| >=1.5.1 | pandas |
| >=1.1.3 | scikit-learn |
| >=0.3 | Boruta |
| - | pip-tools |
| - | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl blitzml-0.9.0:
pip install blitzml-0.9.0.whl
نصب پکیج tar.gz blitzml-0.9.0:
pip install blitzml-0.9.0.tar.gz