Created on Mon Aug 23 20:11:45 2021
@author: henry
"""
# In[]
import HappyML.preprocessor as pp
dataset = pp.dataset(file="Mushrooms.csv")
X, Y = pp.decomposition(dataset,
x_columns=[i for i in range(1, 23)],
y_columns=[0])
X = pp.onehot_encoder(X, columns=[i for i in range(22)], remove_trap=True)
Y, Y_mapping = pp.label_encoder(Y, mapping=True)
from HappyML.preprocessor import KBestSelector
selector = KBestSelector(best_k="auto")
X = selector.fit(x_ary=X, y_ary=Y, verbose=True, sort=True).transform(x_ary=X)
X_train, X_test, Y_train, Y_test = pp.split_train_test(x_ary=X, y_ary=Y)
# In[]
from HappyML.classification import DecisionTree
classifier = DecisionTree()
Y_pred = classifier.fit(X_train, Y_train).predict(X_test)
# In[]
@author: 俊男 """ # In[] Preprocessing import HappyML.preprocessor as pp # Load Data dataset = pp.dataset(file="Mushrooms.csv") # Decomposition X, Y = pp.decomposition(dataset, x_columns=[i for i in range(1, 23)], y_columns=[0]) # Dummy Variables X = pp.onehot_encoder(X, columns=[i for i in range(22)], remove_trap=True) Y = pp.label_encoder(Y) # Feature Selection from HappyML.preprocessor import KBestSelector selector = KBestSelector(best_k="auto") 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 (optional) 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