# 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)
# 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))
# In[] Multiple Linear Regression
## Add one column X0 for constant C0
#import statsmodels.tools.tools as smtools
#X_train = smtools.add_constant(X_train)
#
## Try-and-Error of Selecting Features with Backward Elimination
#import statsmodels.api as sm
#
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))
# In[]
from HappyML.regression import MultipleRegressor
X_train = pp.add_constant(X_train)
X_test = pp.add_constant(X_test)
regressor = MultipleRegressor()
selected_features = regressor.backward_elimination(x_train=X_train, y_train=Y_train)
Y_predict = regressor.fit(x_train=X_train.iloc[:, selected_features], y_train=Y_train).predict(x_test=X_test.iloc[:, selected_features])
X, Y = pp.decomposition(dataset, x_columns=[1], y_columns=[2])
# Training / Testing Set
X_train, X_test, Y_train, Y_test = pp.split_train_test(x_ary=X,
y_ary=Y,
train_size=0.8)
# Feature Scaling
#X = pp.feature_scaling(fit_ary=X, transform_arys=(X))
#Y = pp.feature_scaling(fit_ary=Y, transform_arys=(Y))
# In[] Linear Regression as comparison
from HappyML.regression import SimpleRegressor
import HappyML.model_drawer as md
reg_simple = SimpleRegressor()
Y_simple = reg_simple.fit(x_train=X, y_train=Y).predict(x_test=X)
md.sample_model(sample_data=(X, Y), model_data=(X, Y_simple))
print("R-Squared of Simple Regression:", reg_simple.r_score(x_test=X,
y_test=Y))
# In[] Polynomial Regression
#from sklearn.preprocessing import PolynomialFeatures
#from HappyML.performance import rmse
#import pandas as pd
#
#deg=5
#poly_reg = PolynomialFeatures(degree=deg)
#X_poly = pd.DataFrame(poly_reg.fit_transform(X))
#
from HappyML import preprocessor as pp
from HappyML.regression import SimpleRegressor
import pandas as pd
from HappyML import model_drawer as md
dataset_h = pp.dataset("Student_Height.csv")
dataset_w = pp.dataset("Student_Weight.csv")
X_h, Y_h = pp.decomposition(dataset_h, [1], [3, 4])
X_w, Y_w = pp.decomposition(dataset_w, [1], [3, 4])
X_h_train, X_h_test, Y_h_train, Y_h_test = pp.split_train_test(X_h, Y_h)
X_w_train, X_w_test, Y_w_train, Y_w_test = pp.split_train_test(X_w, Y_w)
regressor = [[SimpleRegressor(), SimpleRegressor()],
[SimpleRegressor(), SimpleRegressor()]]
regressor[0][0].fit(X_h_train, Y_h_train.iloc[:, 0].to_frame())
regressor[0][1].fit(X_h_train, Y_h_train.iloc[:, 1].to_frame())
regressor[1][0].fit(X_w_train, Y_w_train.iloc[:, 0].to_frame())
regressor[1][1].fit(X_w_train, Y_w_train.iloc[:, 1].to_frame())
print("台灣 6~15 歲學童身高、體重評估系統\n")
gender = eval(input("請輸入您的性別（1.男 2.女）：")) - 1
age = eval(input("請輸入您的年齡（6-15）："))
height = eval(input("請輸入您的身高（cm）："))
weight = eval(input("請輸入您的體重（kg）："))
h_avg = regressor[0][gender].predict(x_test=pd.DataFrame([[age]])).iloc[0, 0]
w_avg = regressor[1][gender].predict(x_test=pd.DataFrame([[age]])).iloc[0, 0]
transform_arys=(X_train, X_test))
Y_train, Y_test = pp.feature_scaling(fit_ary=Y_train,
transform_arys=(Y_train, Y_test))
# In[] Fitting Simple Regressor
# from sklearn.linear_model import LinearRegression
# regressor = LinearRegression()
# regressor.fit(X_train, Y_train)
# Y_pred = regressor.predict(X_test)
# R_Score = regressor.score(X_test, Y_test)
from HappyML.regression import SimpleRegressor
regressor = SimpleRegressor()
Y_pred = regressor.fit(X_train, Y_train).predict(X_test)
print("R-Squared Score:", regressor.r_score(X_test, Y_test))
# In[] Visualize the Training Set
#import matplotlib.pyplot as plt
#
#plt.scatter(X_train, Y_train, color="red")
#plt.plot(X_train, regressor.predict(X_train), color="blue")
#plt.title("Salary vs. Experience")
#plt.xlabel("Experience")
#plt.ylabel("Salary")
#plt.show()
from HappyML import model_drawer as md
"""
# 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/Position_Salaries.csv")
X, Y = pp.decomposition(dataset, x_columns=[1], y_columns=[2])
X_train, X_test, Y_train, Y_test = pp.split_train_test(x_ary=X, y_ary=Y, train_size=0.8)
# In[]
from HappyML.regression import SimpleRegressor
import HappyML.model_drawer as md
reg_simple = SimpleRegressor()
Y_simple = reg_simple.fit(x_train=X, y_train=Y).predict(X)
md.sample_model(sample_data=(X, Y), model_data=(X, Y_simple))
print("R-Squared of Simple Regression:", reg_simple.r_score(x_test=X, y_test=Y))
# In[]
from sklearn.preprocessing import PolynomialFeatures
deg = 12
poly_reg = PolynomialFeatures(degree=deg)
X_poly = poly_reg.fit_transform(X)
# In[]
import pandas as pd
regressor = SimpleRegressor()