"""
Created on Mon Aug 2 18:25:36 2021
@author: henry
"""
# In[]
import HappyML.preprocessor as pp
dataset = pp.dataset("C:/Users/henry/Desktop/Python Training/Python機器學習/範例原始碼＆「快樂版」函式庫/Ch05 Regression/50_Startups.csv")
X, Y = pp.decomposition(dataset, [0, 1, 2, 3], [4])
# X = pp.onehot_encoder(X, columns=[3])
# X = pp.remove_columns(X, [3])
X = pp.onehot_encoder(X, columns=[3], remove_trap=True)
X_train, X_test, Y_train, Y_test = pp.split_train_test(X, Y, train_size=0.8)
X_train, X_test= pp.feature_scaling(fit_ary=X_train, transform_arys=(X_train, X_test))
Y_train, Y_test= pp.feature_scaling(fit_ary=Y_train, transform_arys=(Y_train, Y_test))
# In[]
from HappyML.regression import SimpleRegressor
simple_reg = SimpleRegressor()
Y_pred_simple = simple_reg.fit(X_train, Y_train).predict(X_test)
# R-Squared always increase in multiple linear regression --> Use Adjusted R-Squared instead
print("Goodness of Model (R-Squared Score):", simple_reg.r_score(X_test, Y_test))
Created on Mon Jul 15 12:21:45 2019
@author: 俊男
"""
# In[] Pre-processing
import HappyML.preprocessor as pp
# Dataset Loading
dataset = pp.dataset("50_Startups.csv")
# Independent/Dependent Variables Decomposition
X, Y = pp.decomposition(dataset, [0, 1, 2, 3], [4])
# Apply One Hot Encoder to Column[3] & Remove Dummy Variable Trap
X = pp.onehot_encoder(X, columns=[3])
X = pp.remove_columns(X, [3])
#X = pp.onehot_encoder(X, columns=[3], remove_trap=True)
# Split Training vs. Testing Set
X_train, X_test, Y_train, Y_test = pp.split_train_test(X, Y, train_size=0.8)
# Feature Scaling (optional)
#X_train, X_test = pp.feature_scaling(fit_ary=X_train, transform_arys=(X_train, X_test))
#Y_train, Y_test = pp.feature_scaling(fit_ary=Y_train, transform_arys=(Y_train, Y_test))
# In[] Create Linear Regressor
from HappyML.regression import SimpleRegressor
simple_reg = SimpleRegressor()
Y_pred_simple = simple_reg.fit(X_train, Y_train).predict(X_test)
Created on Mon Aug 26 21:20:09 2019 @author: 俊男 """ # In[] Preprocessing import HappyML.preprocessor as pp # Load Dataset dataset = pp.dataset(file="Mall_Customers.csv") # Decomposition X = pp.decomposition(dataset, x_columns=[1, 2, 3, 4]) # One-Hot Encoding X = pp.onehot_encoder(ary=X, columns=[0], remove_trap=True) # Feature Scaling (for PCA Feature Selection) X = pp.feature_scaling(fit_ary=X, transform_arys=X) # Feature Selection (PCA) from HappyML.preprocessor import PCASelector selector = PCASelector() X = selector.fit(x_ary=X, verbose=True, plot=True).transform(x_ary=X) # In[] K-Means Clustering with Fixed Clusters = 4 (Without HappyML) # from sklearn.cluster import KMeans # import time # # K-Means Clustering with K=4
"""
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[] Data Pre-processing
from sklearn.datasets import load_iris
import HappyML.preprocessor as pp
# 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
Created on Tue Sep 7 09:57:19 2021
@author: henry
"""
import HappyML.preprocessor as pp
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import pandas as pd
from HappyML.performance import ClassificationPerformance
dataset = pp.dataset("Churn_Modelling.csv")
X, Y = pp.decomposition(dataset, [i for i in range(3, 13)], [13])
X = pp.onehot_encoder(X, [1, 2], True)
selector = pp.KBestSelector()
X = selector.fit(X, Y, True, True).transform(X)
X_train, X_test, Y_train, Y_test = pp.split_train_test(X, Y)
X_train, X_test = pp.feature_scaling(X_train, (X_train, X_test))
classifier = Sequential()
arithmetic_mean = [0, 0]
arithmetic_mean[0] = int((X_train.shape[1] + 1) / 2)
arithmetic_mean[1] = int((arithmetic_mean[0] + 1) / 2)
experience_law = [0, 0]
"""
Created on Sun Aug 29 05:49:05 2021
@author: henry
"""
import HappyML.preprocessor as pp
from HappyML.classification import DecisionTree
from HappyML.performance import KFoldClassificationPerformance
import HappyML.model_drawer as md
from IPython.display import Image, display
dataset = pp.dataset("HR-Employee-Attrition.csv")
X, Y = pp.decomposition(dataset, [i for i in range(35) if i != 1], [1])
X = pp.onehot_encoder(X, [1, 3, 6, 10, 14, 16, 20, 21], True)
Y, Y_mapping = pp.label_encoder(Y, True)
selector = pp.KBestSelector()
X = selector.fit(X, Y, True, True).transform(X)
X_train, X_test, Y_train, Y_test = pp.split_train_test(X, Y)
classifier = DecisionTree()
Y_pred = classifier.fit(X_train, Y_train).predict(X_test)
K = 10
kfp = KFoldClassificationPerformance(X, Y, classifier.classifier, K)
print("----- Decision Tree Classification -----")
print("{} Folds Mean Accuracy: {}".format(K, kfp.accuracy()))