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AI4Water-1.6
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مشاهده بیشترتوضیحات
Platform for developing data driven based models for sequential/tabular data
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | AI4Water-1.6 |
| نام | AI4Water |
| نسخه کتابخانه | 1.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Ather Abbas |
| ایمیل نویسنده | ather_abbas786@yahoo.com |
| آدرس صفحه اصلی | https://github.com/AtrCheema/AI4Water |
| آدرس اینترنتی | https://pypi.org/project/AI4Water/ |
| مجوز | - |
# AI4Water
[](https://github.com/AtrCheema/AI4Water/actions)
[](https://ai4water.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5194/gmd-15-3021-2022)
[](https://pepy.tech/project/ai4water)
[](https://badge.fury.io/py/AI4Water)
A uniform and simplified framework for rapid experimentation with deep leaning and machine learning based models
for time series and tabular data. To put into Andrej Karapathy's [words](https://twitter.com/karpathy/status/1350503355299205120)
`Because deep learning is so empirical, success in it is to a large extent proportional to raw experimental throughput,
the ability to babysit a large number of experiments at once, staring at plots and tweaking/re-launching what works.
This is necessary, but not sufficient.`
The specific purposes of the repository are
- compliment the functionality of `keras`/`pytorch`/`sklearn` by making pre and
post-processing easier for time-series prediction/classification problems (also holds
true for any tabular data).
- save, load/reload or build models from readable json file. This repository
provides a framework to build layered models using python dictionary and with
several helper tools which fasten the process of modeling time-series forecasting.
- provide a uniform interface for optimizing hyper-parameters for
[skopt](https://scikit-optimize.github.io/stable/index.html);
[sklearn](https://scikit-learn.org/stable/modules/classes.html#hyper-parameter-optimizers)
based [grid](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)
and [random](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html);
[hyperopt](http://hyperopt.github.io/hyperopt/) based
[tpe](https://papers.nips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf),
[atpe](https://www.electricbrain.io/blog/learning-to-optimize) or
[optuna](https://optuna.readthedocs.io/en/stable/) based
[tpe](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.samplers.TPESampler.html),
[cmaes](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.samplers.CmaEsSampler.html) etc.
See [example](https://github.com/AtrCheema/AI4Water/blob/master/examples/hyper_para_opt.ipynb)
using its application.
- cut short the time to write boilerplate code in developing machine learning
based models.
- It should be possible to overwrite/customize any of the functionality of the AI4Water's `Model`
by subclassing the
`Model`. So at the highest level you just need to initiate the `Model`, and then need `fit`, `predict` and
`view_model` methods of `Model` class, but you can go as low as you could go with tensorflow/keras.
- All the above functionalities should be available without complicating keras
implementation.
## Installation
An easy way to install ai4water is using pip
pip install ai4water
You can also use GitHub link
python -m pip install git+https://github.com/AtrCheema/AI4Water.git
or using setup file, go to folder where repo is downloaded
python setup.py install
The latest code however (possibly with fewer bugs and more features) can be installed from `dev` branch instead
python -m pip install git+https://github.com/AtrCheema/AI4Water.git@dev
To install the latest branch (`dev`) with all requirements use the following command
python -m pip install "AI4Water[all] @ git+https://github.com/AtrCheema/AI4Water.git@dev"
### installation options
`all` keyword will install all the dependencies. You can choose the dependencies of particular sub-module
by using the specific keyword. Following keywords are available
- `hpo` if you want hyperparameter optimization
- `post_process` if you want postprocessing
- `exp` for experiments sub-module
## Sub-modules
AI4Water consists of several submodules, each of wich responsible for a specific tasks.
The modules are also liked with each other. For understanding sub-module structure of
ai4water, [see this article](https://ai4water.readthedocs.io/en/dev/understanding.html)
<p float="left">
<img src="/docs/source/imgs/architecture.png" width="800" height="700"/>
</p>
## How to use
Build a `Model` by providing all the arguments to initiate it.
```python
from ai4water import Model
from ai4water.models import MLP
from ai4water.datasets import mg_photodegradation
data, *_ = mg_photodegradation(encoding="le")
model = Model(
# define the model/algorithm
model=MLP(units=24, activation="relu", dropout=0.2),
# columns in data file to be used as input
input_features=data.columns.tolist()[0:-1],
# columns in csv file to be used as output
output_features=data.columns.tolist()[-1:],
lr=0.001, # learning rate
batch_size=8, # batch size
epochs=500, # number of epochs to train the neural network
patience=50, # used for early stopping
)
```
Train the model by calling the `fit()` method
```python
history = model.fit(data=data)
```
<p float="left">
<img src="/docs/source/imgs/mlp_loss.png" width="500" />
</p>
After training, we can make predictions from it on test/training data
```python
prediction = model.predict_on_test_data(data=data)
```
<p float="left">
<img src="/docs/source/imgs/mlp_reg.png" width="400" />
<img src="/docs/source/imgs/mlp_residue.png" width="400" />
</p>
<p float="left">
<img src="/docs/source/imgs/mlp_line.png" width="400" />
<img src="/docs/source/imgs/mlp_edf.png" width="400" />
</p>
The model object returned from initiating AI4Water's `Model` is same as that of Keras' `Model`
We can verify it by checking its type
```python
import tensorflow as tf
isinstance(model, tf.keras.Model) # True
```
## Using your own pre-processed data
You can use your own pre-processed data without using any of pre-processing tools of AI4Water. You will need to provide
input output paris to `data` argument to `fit` and/or `predict` methods.
```python
import numpy as np
from ai4water import Model # import any of the above model
from ai4water.models import LSTM
batch_size = 16
lookback = 15
inputs = ['dummy1', 'dummy2', 'dummy3', 'dummy4', 'dummy5'] # just dummy names for plotting and saving results.
outputs=['DummyTarget']
model = Model(
model = LSTM(units=64),
batch_size=batch_size,
ts_args={'lookback':lookback},
input_features=inputs,
output_features=outputs,
lr=0.001
)
x = np.random.random((batch_size*10, lookback, len(inputs)))
y = np.random.random((batch_size*10, len(outputs)))
model.fit(x=x,y=y)
```
## using for `scikit-learn`/`xgboost`/`lgbm`/`catboost` based models
The repository can also be used for machine learning based models such as scikit-learn/xgboost based models for both
classification and regression problems by making use of `model` keyword arguments in `Model` function.
However, integration of ML based models is not complete yet.
```python
from ai4water import Model
from ai4water.datasets import busan_beach
data = busan_beach() # path for data file
model = Model(
# columns in data to be used as input
input_features=['tide_cm', 'wat_temp_c', 'sal_psu', 'rel_hum', 'pcp_mm'],
output_features = ['tetx_coppml'], # columns in data file to be used as input
seed=1872,
val_fraction=0.0,
split_random=True,
# any regressor from https://scikit-learn.org/stable/modules/classes.html
model={"RandomForestRegressor": {}}, # set any of regressor's parameters. e.g. for RandomForestRegressor above used,
# some of the paramters are https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor
)
history = model.fit(data=data)
model.predict_on_test_data(data=data)
```
# Hyperparameter optimization
For hyperparameter optimization, replace the actual values of hyperparameters
with the space.
```python
from ai4water.functional import Model
from ai4water.datasets import MtropicsLaos
from ai4water.hyperopt import Real, Integer
data = MtropicsLaos().make_regression(lookback_steps=1)
model = Model(
model = {"RandomForestRegressor": {
"n_estimators": Integer(low=5, high=30, name='n_estimators', num_samples=10),
"max_leaf_nodes": Integer(low=2, high=30, prior='log', name='max_leaf_nodes', num_samples=10),
"min_weight_fraction_leaf": Real(low=0.0, high=0.5, name='min_weight_fraction_leaf', num_samples=10),
"max_depth": Integer(low=2, high=10, name='max_depth', num_samples=10),
"min_samples_split": Integer(low=2, high=10, name='min_samples_split', num_samples=10),
"min_samples_leaf": Integer(low=1, high=5, name='min_samples_leaf', num_samples=10),
}},
input_features=data.columns.tolist()[0:-1],
output_features=data.columns.tolist()[-1:],
cross_validator = {"KFold": {"n_splits": 5}},
x_transformation="zscore",
y_transformation="log",
)
# First check the performance on test data with default parameters
model.fit_on_all_training_data(data=data)
print(model.evaluate_on_test_data(data=data, metrics=["r2_score", "r2"]))
# optimize the hyperparameters
optimizer = model.optimize_hyperparameters(
algorithm = "bayes", # you can choose between `random`, `grid` or `tpe`
data=data,
num_iterations=60,
)
# Now check the performance on test data with default parameters
print(model.evaluate_on_test_data(data=data, metrics=["r2_score", "r2"]))
```
Running the above code will optimize the hyperparameters and generate
following figures
<p float="left">
<img src="/docs/source/imgs/hpo_ml_convergence.png" width="400" />
<img src="/docs/source/imgs/hpo_fanova_importance_hist.png" width="400" />
</p>
<p float="left">
<img src="/docs/source/imgs/hpo_objective.png" width="500" />
<img src="/docs/source/imgs/hpo_evaluations.png" width="500" />
</p>
<p float="left">
<img src="/docs/source/imgs/hpo_parallel_coordinates.png" width="500" />
</p>
# Experiments
The experiments module is for comparison of multiple models on a single data
or for comparison of one model under different conditions.
```python
from ai4water.datasets import busan_beach
from ai4water.experiments import MLRegressionExperiments
data = busan_beach()
comparisons = MLRegressionExperiments(
input_features=data.columns.tolist()[0:-1],
output_features=data.columns.tolist()[-1:],
split_random=True
)
# train all the available machine learning models
comparisons.fit(data=data)
# Compare R2 of models
best_models = comparisons.compare_errors(
'r2',
data=data,
cutoff_type='greater',
cutoff_val=0.1,
figsize=(8, 9),
colors=['salmon', 'cadetblue']
)
# Compare model performance using Taylor diagram
_ = comparisons.taylor_plot(
data=data,
figsize=(5, 9),
exclude=["DummyRegressor", "XGBRFRegressor",
"SGDRegressor", "KernelRidge", "PoissonRegressor"],
leg_kws={'facecolor': 'white',
'edgecolor': 'black','bbox_to_anchor':(2.0, 0.9),
'fontsize': 10, 'labelspacing': 1.0, 'ncol': 2
},
)
```
<p float="left">
<img src="/docs/source/imgs/exp_r2.png" width="500" />
<img src="/docs/source/imgs/exp_taylor.png" width="500" />
</p>
For more comprehensive and detailed examples see [](https://ai4water.readthedocs.io/projects/Examples/en/latest/?badge=latest)
## Disclaimer
The library is still under development. Fundamental changes are expected without prior notice or
without regard of backward compatability.
#### Related
[sktime: A Unified Interface for Machine Learning with Time Series](https://github.com/alan-turing-institute/sktime)
[Seglearn: A Python Package for Learning Sequences and Time Series](https://github.com/dmbee/seglearn)
[Pastas: Open Source Software for the Analysis of Groundwater Time Series](https://github.com/pastas/pastas)
[Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh -- A Python package)](https://github.com/blue-yonder/tsfresh)
[MLAir](https://gmd.copernicus.org/preprints/gmd-2020-332/)
[pyts: A Python Package for Time Series Classification](https://github.com/johannfaouzi/pyts)
[Tslearn, A Machine Learning Toolkit for Time Series Data](https://github.com/tslearn-team/tslearn)
[TSFEL: Time Series Feature Extraction Library](https://doi.org/10.1016/j.softx.2020.100456)
[catch22](https://github.com/chlubba/catch22)
[vest](https://github.com/vcerqueira/vest-python)
[pyunicorn (Unified Complex Network and RecurreNce analysis toolbox](https://github.com/pik-copan/pyunicorn)
[TSFuse Python package for automatically constructing features from multi-view time series data](https://github.com/arnedb/tsfuse)
[Catalyst](https://github.com/catalyst-team/catalyst)
[tsai - A state-of-the-art deep learning library for time series and sequential data](https://github.com/timeseriesAI/tsai)
نحوه نصب
نصب پکیج whl AI4Water-1.6:
pip install AI4Water-1.6.whl
نصب پکیج tar.gz AI4Water-1.6:
pip install AI4Water-1.6.tar.gz