semvec/pypi-packages huggingface dataset:
fast-ensemble 0.0.5
Library for high level model ensembling
License: MIT
Requires Python: >=3.7

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# fast-ensemble
Scikit-learn-style library for effecient and convenient high level table model ensembling

---
## Usage Example:

Initialize Stack
```python
from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from sklearn.datasets import make_regression
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor

from fast_ensemble import StackingTransformer
from fast_ensemble import (
    CatBoostRegressorWrapper,
    LGBMRegressorWrapper,
    XGBRegressorWrapper,
)

stack_1 = StackingTransformer(
    models=[
        (
            "catboost",
            CatBoostRegressorWrapper(
                CatBoostRegressor(verbose=0),
                use_best_model=True,
                early_stopping_rounds=100,
            ),
        ),
        (
            "xgboost",
            XGBRegressorWrapper(
                XGBRegressor(), use_best_model=True, early_stopping_rounds=100
            ),
        ),
        (
            "lgmb",
            LGBMRegressorWrapper(
                LGBMRegressor(), use_best_model=True, early_stopping_rounds=100
            ),
        ),
        ("boosting", GradientBoostingRegressor()),
    ],
    main_metric=mean_squared_error,
    regression=True,
    n_folds=5,
    random_state=None,
    shuffle=False,
    verbose=True,
    stratified=True,
    stratification_bins=7
)
```
And another one

```python
stack_2 = StackingTransformer(
    models=[
        ("Dummy Regressor", DummyRegressor()),
    ],
    main_metric=mean_squared_error,
    regression=True,
    n_folds=5,
    random_state=None,
    shuffle=False,
    verbose=True,
    stratified=True,
    stratification_bins=7
)
```
Train your stacks (and get transformed dataframes)
```python
X, y = make_regression(n_targets=1)

X_1_trans = stack_1.fit_transform(X, y)
X_2_trans = stack_2.fit_transform(X, y)
```
Want to merge 2 stacks for convenience? Here you go!
```python
stack_1.merge(stack_2)

stack_1.get_scores(prettified=True)

       catboost       xgboost          lgmb      boosting  Dummy Regressor
0   9852.055535  23389.781003   8872.055479  13130.504063    21344.359900
1  14259.407424  20177.587908  12040.548492  14088.529604    28620.260635
2  16393.254421  24267.409682   9503.011118  15067.349045    33377.287468
3  12694.791124  16349.931831   7188.301326  10675.853608    29510.019041
4  17505.264716  12158.834533  10273.547605   9621.041119    39099.670810
```
noto 0.0.1
Non-Deterministic Note Box
License: MIT
Requires Python: >=3.7
Keywords: notes,fun

# noto - A non-deterministic note box

noto is a small CLI application, that allows you to very quickly store and edit notes on your local machine
and read them again at your leisure.

You can **prioritize** and **tag** your notes, as well as **update** and **delete** them. The application is designed to be as fast and simple to use on the command line.

## Installation

## Examples

### Creating a Note

``` bash
noto write "Hello World!"
> Note(id=1, text=Hello World!)
```

You may also assign a priority and/or tags on creation:

``` bash
noto write "Hello Moon!" -p 9000 -t foo -t bar
> Note(id=2, text=Hello Moon!, priority=9000, tags=[foo, bar])
```

### Interactive Mode

noto is designed to make it easy for you to journal through a stream of random notes.

> You can configure the behaviour of the interactive mode - including whether to return highest prio notes instead of random ones - by editing the `config.yaml`.

To get started, do this:

``` bash
noto
```

You will be presented with a random note that you've written in the past.

``` bash
Note(id=512, text=Whazzup?)
```

In addition, you will be presented with options to modify that note:

``` bash
Modifying note: Confirm (Enter), Content (c), Priority (p), Remove Tag (t), Add Tag (T), Delete Note (D), Quit (q)
```

### Manual Mode

As an alternative, you can edit your notes as well using specific commands:

``` bash
noto write
```

``` bash
noto read
```

``` bash
noto update
```

``` bash
noto delete
```

See their respective `--help` commands for further information.