Installation¶

Install the current PyPI release:
```
$ pip install probnet==0.2.0
```

Install directly from source code:

$ git clone https://github.com/thieu1995/ProbNet.git
$ cd ProbNet
$ python setup.py install

In case, you want to install the development version from Github:
```
$ pip install git+https://github.com/thieu1995/ProbNet
```

After installation, you can import MetaPerceptron as any other Python module:

$ python
>>> import probnet
>>> probnet.__version__

Examples¶

In this section, we will explore the usage of the Adam-based Gradient Optimizer for training ANFIS networks:

from probnet import PnnClassifier, Data
from sklearn.datasets import load_breast_cancer


## Load data object
X, y = load_breast_cancer(return_X_y=True)
data = Data(X, y)

## Split train and test
data.split_train_test(test_size=0.2, random_state=2, inplace=True)
print(data.X_train.shape, data.X_test.shape)

## Scaling dataset
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "minmax"))
data.X_test = scaler_X.transform(data.X_test)

data.y_train, scaler_y = data.encode_label(data.y_train)
data.y_test = scaler_y.transform(data.y_test)

## Train and test
# model = PnnClassifier(sigma=1.0, kernel='laplace', dist='manhattan', normalize_output=True)
model = PnnClassifier(sigma=1.0)
model.fit(data.X_train, data.y_train)

## Predict
print("Predicted:", model.predict(data.X_test))
print("Probabilities:\n", model.predict_proba(data.X_test))

## Calculate some metrics
print(model.score(X=data.X_test, y=data.y_test))
print(model.scores(X=data.X_test, y=data.y_test, list_metrics=["PS", "RS", "NPV", "F1S", "F2S"]))
print(model.evaluate(y_true=data.y_test, y_pred=model.predict(data.X_test), list_metrics=["F2S", "CKS", "FBS"]))

A real-world dataset contains features that vary in magnitudes, units, and range. We would suggest performing normalization when the scale of a feature is irrelevant or misleading. Feature Scaling basically helps to normalize the data within a particular range.