semvec/pypi-packages huggingface dataset:
whirly 1.0.1
Spectral solutions to two-dimensional PDEs.

# whirly

whirly provides easy-to-use Python classes for the spectral solution of two-dimensional partial differential equations, especially those related to fluid dynamics. The core of the package is the abstract `PseudospectralSolver` class, which is easily extensible to a variety of PDEs with linear and nonlinear terms. For time stepping, whirly implements a fourth-order integrating factor method as outlined in [Yang et al. (2021)](https://www.sciencedirect.com/science/article/pii/S002199912030766X), accessible through the `IFRK4Integrator` class.

See [`example.ipynb`](https://github.com/dsconnelly/whirly/blob/main/example.ipynb) for a quick demonstration of whirly used to solve the two-dimensional [vorticity equation](https://en.wikipedia.org/wiki/Vorticity_equation).

whirly can be installed with `pip install whirly`.
praxis-timeseries-client 0.0.4
A Darts Time Series client for facilitating model development and data exploration.
Requires Python: >=3.7

# Praxis Time Series Client

Current version: 0.0.4
Python version: >= 3.7

A package for quick visualization of a target Darts time series objects alongside covariates and discrete data.

## Usage

The main component of this package is the `DartsInterface` class found in `praxis_timeseries_client.darts`. This synthesizes together your Time Series objects under the same hood, so you can interact with them together in a single graph and run models in a plug-and-play manner with your covariates.

```python
from praxis_timeseries_client.darts import DartsInterface
from darts import TimeSeries

interface = DartsInterface(
    target_ts=TimeSeries.from_dataframe(...),
    past_covariates=TimeSeries.from_dataframe(...),
    future_covariates=TimeSeries.from_dataframe(...),
    discrete_vars={...})
```

Now, you can plot with the interface:

```python
interface.plot(components=[...]).show() # Plot all components, or an ordered subset
```

And run forecasts and backtests with a model pre-trained on the covariates:

```python
# make sure ts_train == target_ts from above
model = LinearRegressionModel(lags=50, lags_future_covariates=(100, 50), output_chunk_length=30)
model.fit(series=ts_train, future_covariates=ts_future_covs)

# run a backtest and plot it
backtest = interface.backtest(
    model,
    start=pd.Timestamp(startDate),
    forecast_horizon=int(forecastHorizon),
    stride=int(stride))
backtest.run(past_covariates=None)
backtest.plot().show()

# or run a forecast and plot it
forecast = interface.forecast(model, n=50)
forecast.run(past_covariates=None)
forecast.plot().show()
```

Hopefully this is enough to get you started using the interfaces!
machineLearningModelsPKG 1.0.2
Machine Learning models make simple
Keywords: python,classification,regression,machine learning models,hyper parameter turning

# Project Title

Machine Learning Models Made Simple

## Guide

### Classfication model

    Classifier->(X,Y,test_size=0.2,random_state=20,scaler=None)[class]
                [function]
                model_training
                model_accuracy(y_test_f,y_pred_f,model_name,model_obj)

                logistic_regression
                kneighbors_classifier
                gaussian_nb
                bagging_classifier
                extra_trees_classifier
                ridge_classifier
                sgd_classifier
                random_forest_classifier
                xgb_classifier
                ada_boost_classifier
                bernoulli_nb
                gradient_boosting_classifier
                decision_tree_classifier
                svc

                [Hyperparameter tuning]

                hyperparameter_tuning

                logistic_hyperparameter
                knn_hyperparameter
                gaussian_nb_hyperparameter
                bernoulli_nb_hyperparameter
                ridge_hyperparameter
                adaboost_hyperparameter
                gradient_boosting_hyperparameter
                svc_hyperparameter
                decision_tree_hyperparameter

### Regression model

    Regressor->(X,Y,test_size=0.2,random_state=20,scaler=None)[class]
                [function]
                model_training
                model_accuracy(y_test_f,y_pred_f,model_name,model_obj)

                linear_regression
                ridge_regression
                lasso_regression
                elastic_net_regression
                sgd_regression
                random_forest_regression
                kneighbors_regression
                decision_tree_regression
                ada_boost_regression
                xgboost_regression
                gradient_boosting_regression
                theilsen_regression
                ransac_regression
                lasso_lars_regression
                lars_regression
                orthogonal_regression
                huber_regression
                svr
                passive_aggressive_regression
                ard_regression
                bayesian_ridge_regression
                bagging_regression
                extra_trees_regression