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fastreport-0.0.6
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-
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fastreport-0.0.6 |
| نام | fastreport |
| نسخه کتابخانه | 0.0.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Kishore |
| ایمیل نویسنده | <kishoresshankar@gmail.com> |
| آدرس صفحه اصلی | https://github.com/kishore-s-gowda/fastreport |
| آدرس اینترنتی | https://pypi.org/project/fastreport/ |
| مجوز | MIT |
# Fastreport
Get report of different metrices for classification and regression problem for many popular algorithms with single line of code. You have to pass only features(dataframe) and target(series) as arguments
Link to [PyPI](https://pypi.org/project/fastreport/)
Link to [Classification detailed example](https://github.com/kishore-s-gowda/fastreport/blob/master/Classification%20example.ipynb)
Link to [Regression detailed example](https://github.com/kishore-s-gowda/fastreport/blob/master/Regression%20Problem.ipynb)
## Installation
Run the following to install:
```python
pip install fastreport
```
### Install sklearn and xgboost
```python
pip install sklearn
```
```python
pip install xgboost
```
## Usage
### Classification
```python
import report
report.report_classification(df_features,df_target,algorithms='default',test_size=0.3,scaling=None,
large_data=False,encode='dummy',average='binary',change_data_type = False,
threshold=8,random_state=None):
```
parameters
----------------------------
df_features : Pandas DataFrame
df_target : Pandas Series
algorithms : List ,'default'=
[LogisticRegression(),
GaussianNB(),
DecisionTreeClassifier(),
RandomForestClassifier(),
GradientBoostingClassifier(),
AdaBoostClassifier(),
XGBClassifier()]
The above are the default algorithms, if one needs any specific algorithms, they have to import
libraries then pass the instances of alogorith as list
For example, if one needs random forest and adaboost only, then pass
algorithms=[RandomForestClassifier(max_depth=8),AdaBoostClassifier()]
But, these libraries must be imported before passing into above list like
test_size: If float, should be between 0.0 and 1.0 and represent the proportion of the
dataset to include in the test split.
scaling : {'standard-scalar', 'min-max'} or None , default=None
encode : {'dummy','onehot','label'} ,default='dummy'
change_data_type : bool, default=False
Some columns will be of numerical datatype though there are only 2-3 unique values in that column,
so these columns must be converted to object as it is more relevant.
By setting change_data_type= True , these columns will be converted into object datatype
threshold : int ,default=8
Maximum unique value a column can have
large_data : bool, default=False
If the dataset is large then the parameter large_data should be set to True,
make sure if your system has enough memory before setting Large_data=True
average : {'micro', 'macro', 'samples','weighted', 'binary'} or None, default='binary'
This parameter is required for multiclass/multilabel targets.
If ``None``, the scores for each class are returned. Otherwise, this
determines the type of averaging performed on the data:
``'binary'``:
Only report results for the class specified by ``pos_label``.
This is applicable only if targets (``y_{true,pred}``) are binary.
``'micro'``:
Calculate metrics globally by counting the total true positives,
false negatives and false positives.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
``'samples'``:
Calculate metrics for each instance, and find their average (only
meaningful for multilabel classification where this differs from
:func:`accuracy_score`).
random_state : int, RandomState instance or None, default=None
### Regression
```python
import report
report.report_regression(df_features,df_target,algorithms='default',test_size=0.3,
scaling=None,large_data=False,change_data_type=True,encode='dummy',
threshold=8,random_state=None):
```
parameters
----------------------------
df_features : Pandas DataFrame
df_target : Pandas Series
algorithms : List ,'default'=
[LinearRegression(),
Lasso(),
Ridge(),
RandomForestRegressor(),
GradientBoostingRegressor(),
AdaBoostRegressor(),
XGBRegressor]
The above are the default algorithms, if one needs any specific algorithms, they have to import
libraries then pass the instances of alogorith as list
For example, if one needs random forest and adaboost only, then pass
algorithms=[RandomForestRegressor(max_depth=8),AdaBoostRegressor()]
But, these libraries must be imported before passing into above list like
test_size: If float, should be between 0.0 and 1.0 and represent the proportion of the
dataset to include in the test split.
scaling : {'Standard-scalar', 'min-max'} or None , default=None
encode : {'dummy','onehot','label'} ,default='dummy'
change_data_type : bool, default=False
Some columns will be of numerical datatype though there are only 2-3 unique values in that column,
so these columns must be converted to object as it is more relevant.
By setting change_data_type= True , these columns will be converted into object datatype
threshold : int ,default=8
Maximum unique value a column can have
large_data : bool, default=False
If the dataset is large then the parameter large_data should be set to True,
make sure if your system has enough memory before setting Large_data=True
random_state : int, RandomState instance or None, default=None
## Future works
1. Optimization
2. Add more functionality
## Drawbacks
1. Not suitable for very large datasets
2. Limited to existing users only
## License
© 2021 KISHORE S
This repository is licensed under the MIT license. See LICENSE for details.
نحوه نصب
نصب پکیج whl fastreport-0.0.6:
pip install fastreport-0.0.6.whl
نصب پکیج tar.gz fastreport-0.0.6:
pip install fastreport-0.0.6.tar.gz