def train_linear(): yelp_data = data_importer.get_yelp_data() test_data = np.array(yelp_data) # print test_data yelp_x = yelp_data['data'] yelp_y = yelp_data['target'] yelp_x_train = yelp_x[:len(yelp_x)*80/100] yelp_y_train = yelp_y[:len(yelp_y)*80/100] yelp_x_test = yelp_x[(len(yelp_x)*80/100)+1:] yelp_y_test = yelp_y[(len(yelp_y)*80/100)+1:] print yelp_x_test[:10] print yelp_y_test[:10] # Create linear regression object regr = linear_model.LinearRegression() # Train the model using the training sets regr.fit(yelp_x_train, yelp_y_train) # The coefficients print('Coefficients: \n', regr.coef_) # The mean square error print("Residual sum of squares: %.2f"% np.mean((regr.predict(yelp_x_test) - yelp_y_test) ** 2)) # Explained variance score: 1 is perfect prediction print('Variance score: %.2f' % regr.score(yelp_x_test, yelp_y_test)) # print( len(yelp_x)) # print len(yelp_x_test) # print len(yelp_y_test) # Plot outputs print regr.predict([1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 2, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0]) return regr
def train_svm_rbf(): yelp_data = data_importer.get_yelp_data() test_data = np.array(yelp_data) # print test_data yelp_x = yelp_data['data'] yelp_y = yelp_data['target'] #print yelp_x #print yelp_y yelp_x_train = yelp_x[:len(yelp_x) * 85 / 100] yelp_y_train = yelp_y[:len(yelp_y) * 85 / 100] yelp_x_test = yelp_x[(len(yelp_x) * 85 / 100) + 1:] yelp_y_test = yelp_y[(len(yelp_y) * 85 / 100) + 1:] # print yelp_x_test[:10] # print yelp_y_test[:10] svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1) # -svr_poly = SVR(kernel='rbf', C=1e3, gamma=0.1) t0 = time.time() print('Training SVR algorithm with training data started.') print 'Training in Progress...... Please Wait' # - y_rbf = svr_rbf.fit(yelp_x, yelp_y) y_rbf = svr_rbf.fit(yelp_x_train, yelp_y_train) svr_fit = time.time() - t0 print("SVR complexity and bandwidth selected and model fitted in %.3f s" % svr_fit) # # The coefficients # print('Coefficients: \n', regr.coef_) # # The mean square error # print("Residual sum of squares: %.2f"% np.mean((regr.predict(yelp_x_test) - yelp_y_test) ** 2)) # # Explained variance score: 1 is perfect prediction print('Variance score: %.2f' % svr_rbf.score(yelp_x_test, yelp_y_test)) # print svr_rbf.predict([1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 2, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0]) return y_rbf
def train_linear(): yelp_data = data_importer.get_yelp_data() test_data = np.array(yelp_data) # print test_data yelp_x = yelp_data['data'] yelp_y = yelp_data['target'] yelp_x_train = yelp_x[:len(yelp_x) * 80 / 100] yelp_y_train = yelp_y[:len(yelp_y) * 80 / 100] yelp_x_test = yelp_x[(len(yelp_x) * 80 / 100) + 1:] yelp_y_test = yelp_y[(len(yelp_y) * 80 / 100) + 1:] print yelp_x_test[:10] print yelp_y_test[:10] # Create linear regression object regr = linear_model.LinearRegression() # Train the model using the training sets regr.fit(yelp_x_train, yelp_y_train) # The coefficients print('Coefficients: \n', regr.coef_) # The mean square error print("Residual sum of squares: %.2f" % np.mean( (regr.predict(yelp_x_test) - yelp_y_test)**2)) # Explained variance score: 1 is perfect prediction print('Variance score: %.2f' % regr.score(yelp_x_test, yelp_y_test)) # print( len(yelp_x)) # print len(yelp_x_test) # print len(yelp_y_test) # Plot outputs print regr.predict([ 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 2, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0 ]) return regr
def train_svm_poly(): yelp_data = data_importer.get_yelp_data() test_data = np.array(yelp_data) # print test_data yelp_x = yelp_data['data'] yelp_y = yelp_data['target'] print yelp_x print yelp_y # yelp_x_train = yelp_x[:len(yelp_x)*80/100] # yelp_y_train = yelp_y[:len(yelp_y)*80/100] # yelp_x_test = yelp_x[(len(yelp_x)*80/100)+1:] # yelp_y_test = yelp_y[(len(yelp_y)*80/100)+1:] # print yelp_x_test[:10] # print yelp_y_test[:10] svr_poly = SVR(kernel='poly', C=1e3, degree=2) t0 = time.time() print('Training SVR algorithm with training data started.') y_poly = svr_poly.fit(yelp_x, yelp_y) svr_fit = time.time() - t0 print("SVR complexity and bandwidth selected and model fitted in %.3f s" % svr_fit) # # The coefficients # print('Coefficients: \n', regr.coef_) # # The mean square error # print("Residual sum of squares: %.2f"% np.mean((regr.predict(yelp_x_test) - yelp_y_test) ** 2)) # # Explained variance score: 1 is perfect prediction # print('Variance score: %.2f' % regr.score(yelp_x_test, yelp_y_test)) # # print( len(yelp_x)) # # print len(yelp_x_test) # # print len(yelp_y_test) # # Plot outputs # print regr.predict([1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 2, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0]) return y_poly
def train_svm_rbf(): yelp_data = data_importer.get_yelp_data() test_data = np.array(yelp_data) # print test_data yelp_x = yelp_data['data'] yelp_y = yelp_data['target'] print yelp_x print yelp_y # yelp_x_train = yelp_x[:len(yelp_x)*80/100] # yelp_y_train = yelp_y[:len(yelp_y)*80/100] # yelp_x_test = yelp_x[(len(yelp_x)*80/100)+1:] # yelp_y_test = yelp_y[(len(yelp_y)*80/100)+1:] # print yelp_x_test[:10] # print yelp_y_test[:10] svr_poly = SVR(kernel='rbf', C=1e3, gamma=0.1) t0 = time.time() print('Training SVR algorithm with training data started.') y_poly = svr_poly.fit(yelp_x, yelp_y) svr_fit = time.time() - t0 print("SVR complexity and bandwidth selected and model fitted in %.3f s" % svr_fit) # # The coefficients # print('Coefficients: \n', regr.coef_) # # The mean square error # print("Residual sum of squares: %.2f"% np.mean((regr.predict(yelp_x_test) - yelp_y_test) ** 2)) # # Explained variance score: 1 is perfect prediction # print('Variance score: %.2f' % regr.score(yelp_x_test, yelp_y_test)) # # print( len(yelp_x)) # # print len(yelp_x_test) # # print len(yelp_y_test) # # Plot outputs # print regr.predict([1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 2, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0]) return y_poly