from os import chdir chdir("C:/Users/pnlawlor/Google Drive/Research/Projects/Mad_Kegel") import numpy as np from fit_GLM import fit_logistic_GLM, plot_logistic_fit, fit_RF, fit_linear_model X_data = np.random.rand(200,200) y_data2 = np.round(X_data[:,0]) y_data = y_data2.ravel() #models, scores, C, kf = fit_logistic_GLM(X_data, y_data,num_cv=10,verbose=False,plot_results=True) # #models2, scores2, num_estimators2, kf2 = fit_RF(X_data,y_data,num_estimators=1000,num_cv=10) models3 = fit_linear_model(X_data,y_data)
#season_labels = season_labels[data_points] results = (.5*(np.sign(y_data)+1)).astype(int) X_data = pp.scale(X_data) #models3, R2_3, loss_scores3, coef3, prob3, kf3, group_keys3 = fit_linear_model( # X_data,y_data, # results,keys, # labels=season_labels) #models3, R2_3, loss_scores3, coef3, prob3, kf3, group_keys3 = fit_linear_model( # X_data,y_data, # results,keys,num_cv=10) models3, R2_3, loss_scores3, coef3, prob3, group_keys3 = fit_linear_model( X_data,y_data, results,keys,num_cv=1) # first 1/3 win pct, # mid 1/3 win pct, # last 1/3 win pct, # last 1/6 win pct, # away win pct, # ppg, # ppg against, # avg pt differential, # stdv points for*losing team, # stdv points against*losing team save_model = False