Installation¶

Install the current PyPI release:
```
$ pip install metaperceptron==1.1.0
```

Install directly from source code:

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

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

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

$ python
>>> import metaperceptron
>>> metaperceptron.__version__

Examples¶

In this section, we will explore the usage of the MetaPerceptron model with the assistance of a dataset. While all the preprocessing steps mentioned below can be replicated using Scikit-Learn, we have implemented some utility functions to provide users with convenience and faster usage.

Combine MetaPerceptron library like a normal library with scikit-learn:

### Step 1: Importing the libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
from metaperceptron import MlpRegressor, MlpClassifier, MhaMlpRegressor, MhaMlpClassifier

#### Step 2: Reading the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

#### Step 3: Next, split dataset into train and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, random_state=100)

#### Step 4: Feature Scaling
scaler_X = MinMaxScaler()
scaler_X.fit(X_train)
X_train = scaler_X.transform(X_train)
X_test = scaler_X.transform(X_test)

le_y = LabelEncoder()  # This is for classification problem only
le_y.fit(y)
y_train = le_y.transform(y_train)
y_test = le_y.transform(y_test)

#### Step 5: Fitting MLP-based model to the dataset

##### 5.1: Use standard MLP model for regression problem
regressor = MlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="MSE",
         max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
regressor.fit(X_train, y_train)

##### 5.2: Use standard MLP model for classification problem
classifer = MlpClassifier(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="NLLL",
         max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
classifer.fit(X_train, y_train)

##### 5.3: Use Metaheuristic-based MLP model for regression problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_REG_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
model = MhaMlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid",
         obj_name="MSE", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
regressor.fit(X_train, y_train)

##### 5.4: Use Metaheuristic-based MLP model for classification problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
classifier = MhaMlpClassifier(hidden_size=50, act1_name="tanh", act2_name="softmax",
         obj_name="CEL", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
classifier.fit(X_train, y_train)

#### Step 6: Predicting a new result
y_pred = regressor.predict(X_test)

y_pred_cls = classifier.predict(X_test)
y_pred_label = le_y.inverse_transform(y_pred_cls)

#### Step 7: Calculate metrics using score or scores functions.
print("Try my AS metric with score function")
print(regressor.score(X_test, y_test, method="AS"))

print("Try my multiple metrics with scores function")
print(classifier.scores(X_test, y_test, list_methods=["AS", "PS", "F1S", "CEL", "BSL"]))

Utilities everything that metaperceptron provided:

### Step 1: Importing the libraries
from metaperceptron import Data, MlpRegressor, MlpClassifier, MhaMlpRegressor, MhaMlpClassifier
from sklearn.datasets import load_digits

#### Step 2: Reading the dataset
X, y = load_digits(return_X_y=True)
data = Data(X, y)

#### Step 3: Next, split dataset into train and test set
data.split_train_test(test_size=0.2, shuffle=True, random_state=100)

#### Step 4: Feature Scaling
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("minmax"))
data.X_test = scaler_X.transform(data.X_test)

data.y_train, scaler_y = data.encode_label(data.y_train)  # This is for classification problem only
data.y_test = scaler_y.transform(data.y_test)

#### Step 5: Fitting MLP-based model to the dataset
##### 5.1: Use standard MLP model for regression problem
regressor = MlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="MSE",
         max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
regressor.fit(data.X_train, data.y_train)

##### 5.2: Use standard MLP model for classification problem
classifer = MlpClassifier(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="NLLL",
         max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
classifer.fit(data.X_train, data.y_train)

##### 5.3: Use Metaheuristic-based MLP model for regression problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_REG_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
model = MhaMlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid",
         obj_name="MSE", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
regressor.fit(data.X_train, data.y_train)

##### 5.4: Use Metaheuristic-based MLP model for classification problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
classifier = MhaMlpClassifier(hidden_size=50, act1_name="tanh", act2_name="softmax",
         obj_name="CEL", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
classifier.fit(data.X_train, data.y_train)

#### Step 6: Predicting a new result
y_pred = regressor.predict(data.X_test)

y_pred_cls = classifier.predict(data.X_test)
y_pred_label = scaler_y.inverse_transform(y_pred_cls)

#### Step 7: Calculate metrics using score or scores functions.
print("Try my AS metric with score function")
print(regressor.score(data.X_test, data.y_test, method="AS"))

print("Try my multiple metrics with scores function")
print(classifier.scores(data.X_test, data.y_test, list_methods=["AS", "PS", "F1S", "CEL", "BSL"]))

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.