# 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) # 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))
@author: 俊男 """ # In[] Preprocessing import HappyML.preprocessor as pp # Load Dataset dataset = pp.dataset(file="Position_Salaries.csv") # Decomposition of Variables 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,
# 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) # Performance from HappyML.performance import KFoldClassificationPerformance K = 10
@author: henry """ 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):"))
# Load Dataset dataset = pp.dataset(file="Social_Network_Ads.csv") # X, Y Decomposition X, Y = pp.decomposition(dataset, x_columns=[1, 2, 3], y_columns=[4]) # Categorical Data Encoding & Remove Dummy Variable Trap X = pp.onehot_encoder(X, columns=[0], remove_trap=True) # Feature Selection from HappyML.preprocessor import KBestSelector selector = KBestSelector() 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, Y) # Feature Scaling X_train, X_test = pp.feature_scaling(fit_ary=X_train, transform_arys=(X_train, X_test)) # In[] Logistic Regression #from sklearn.linear_model import LogisticRegression #import time # ## Model Creation #classifier = LogisticRegression(solver="lbfgs", random_state=int(time.time())) # ## Features Selection #from sklearn.feature_selection import SelectKBest #from sklearn.feature_selection import chi2
# In[] Import & Load data import HappyML.preprocessor as pp dataset = pp.dataset(file="CarEvaluation.csv") # In[] Decomposition X, Y = pp.decomposition(dataset, x_columns=[i for i in range(4)], y_columns=[4]) # In[] Missing Data X = pp.missing_data(X, strategy="mean") # In[] Categorical Data Encoding # Label Encoding Y, Y_mapping = pp.label_encoder(Y, mapping=True) # One-Hot Encoding X = pp.onehot_encoder(X, columns=[0]) # In[] Split Training Set, Testing Set X_train, X_test, Y_train, Y_test = pp.split_train_test(X, Y, train_size=0.8, random_state=0) # In[] Feature Scaling for X_train, X_test X_train, X_test = pp.feature_scaling(X_train, transform_arys=(X_train, X_test))