# Load Data
dataset = load_iris()
# X, Y
import pandas as pd
X = pd.DataFrame(dataset.data, columns=dataset.feature_names)
Y = pd.DataFrame(dataset.target, columns=["Iris_Type"])
Y_name = dataset.target_names.tolist()
# Load HappyML
from HappyML.preprocessor import KBestSelector
import HappyML.preprocessor as pp
# Feature Selection
selector = KBestSelector(best_k=2)
X = selector.fit(x_ary=X, y_ary=Y, verbose=True, sort=True).transform(x_ary=X)
# Split Training / TEsting Set
X_train, X_test, Y_train, Y_test = pp.split_train_test(x_ary=X, y_ary=Y)
# Feature Scaling
X_train, X_test = pp.feature_scaling(fit_ary=X_train,
transform_arys=(X_train, X_test))
# In[] Comparison: Naive Bayes
from HappyML.classification import NaiveBayesClassifier
clr_bayes = NaiveBayesClassifier()
Y_pred_bayes = clr_bayes.fit(X_train, Y_train).predict(X_test)
# Load Data
dataset = load_iris()
# X, Y
import pandas as pd
X = pd.DataFrame(dataset.data, columns=dataset.feature_names)
Y = pd.DataFrame(dataset.target, columns=["Iris_Type"])
Y_name = dataset.target_names.tolist()
# One_hot incoder for Y
Y = pp.onehot_encoder(ary=Y, columns=[0])
# Feature Selection
from HappyML.preprocessor import KBestSelector
selector = KBestSelector(best_k="auto")
X = selector.fit(x_ary=X, y_ary=Y, auto=False, verbose=True,
sort=True).transform(x_ary=X)
# Split Training / Testing Set
X_train, X_test, Y_train, Y_test = pp.split_train_test(x_ary=X, y_ary=Y)
# Feature Scaling
X_train, X_test = pp.feature_scaling(fit_ary=X_train,
transform_arys=(X_train, X_test))
# In[] Neural Networks without HappyML's Class
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
# Initialize the whole Neural Networks