%matplotlib inline

try:
    import ai4water
except (ImportError, ModuleNotFoundError):
    !pip install ai4water[ml]

machine learning for classification

import numpy as np
import pandas as pd

from ai4water import Model
from ai4water.utils.utils import get_version_info
from ai4water.datasets import MtropicsLaos

D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\numpy\.libs\libopenblas.EL2C6PLE4ZYW3ECEVIV3OXXGRN2NRFM2.gfortran-win_amd64.dll
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\numpy\.libs\libopenblas.GK7GX5KEQ4F6UYO3P26ULGBQYHGQO7J4.gfortran-win_amd64.dll
  warnings.warn("loaded more than 1 DLL from .libs:"
D:\C\Anaconda3\envs\tfcpu27_py39\lib\site-packages\sklearn\experimental\enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.
  warnings.warn(

for k,v in get_version_info().items():
    print(f"{k} version: {v}")

python version: 3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:20:16) [MSC v.1916 64 bit (AMD64)]
os version: nt
ai4water version: 1.06
lightgbm version: 3.3.1
tcn version: 3.4.0
catboost version: 0.26
xgboost version: 1.5.0
easy_mpl version: 0.21.2
SeqMetrics version: 1.3.3
tensorflow version: 2.7.0
keras.api._v2.keras version: 2.7.0
numpy version: 1.21.0
pandas version: 1.3.4
matplotlib version: 3.4.3
h5py version: 3.5.0
sklearn version: 1.0.1
shapefile version: 2.3.0
xarray version: 0.20.1
netCDF4 version: 1.5.7
optuna version: 2.10.1
skopt version: 0.9.0
hyperopt version: 0.2.7
plotly version: 5.3.1
lime version: NotDefined
seaborn version: 0.11.2

dataset = MtropicsLaos(save_as_nc=True,  # if set to True, then netcdf must be installed
                       convert_to_csv=False,
                       path="F:\\data\\MtropicsLaos",
                      )

    Not downloading the data since the directory
    F:\data\MtropicsLaos already exists.
    Use overwrite=True to remove previously saved files and download again

data =    dataset.make_classification(lookback_steps=1)

print(data.shape)

Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is deprecated and will raise a KeyError in a future Version.

(258, 9)

data.head()

	air_temp	rel_hum	wind_speed	sol_rad	water_level	pcp	susp_pm	Ecoli_source	Ecoli_mpn100
2011-05-25 16:36:00	22.540455	96.173793	2.2	34.8	144.6	0.0	0.90	2.868160e+15	1.0
2011-05-25 17:06:00	22.541138	96.174943	2.9	6.0	147.8	0.0	0.99	2.868160e+15	1.0
2011-05-25 17:24:00	22.541547	96.175632	2.6	6.0	149.1	0.0	1.33	2.868160e+15	1.0
2011-05-25 17:42:00	22.541957	96.176322	2.3	6.0	151.8	0.0	0.96	2.868160e+15	1.0
2011-05-25 18:06:00	22.542503	96.177241	1.9	5.8	149.3	0.0	0.74	2.868160e+15	1.0

model = Model(
    input_features=data.columns.tolist()[0:-1],
    output_features=data.columns.tolist()[-1:],
    model="XGBClassifier",
    split_random=True,
    x_transformation="zscore",
)

            building ML model for
            classification problem using XGBClassifier

h = model.fit(data=data)

***** Training *****
input_x shape:  (144, 8)
target shape:  (144, 1)
[11:53:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.0/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.

The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].

p = model.predict_on_validation_data(data=data)

***** Validation *****
input_x shape:  (36, 8)
target shape:  (36, 1)