How to use test_not_nan method in hypothesis

Best Python code snippet using hypothesis

ridge_regression.py

Source:ridge_regression.py

1__author__ = 'liangshengzhang'2import process as pr3import numpy as np4import time5# Tried alphas6alphas = np.array([0.1, 0.2, 0.3, 0.5, 0.7, 1, 5, 10])7start_time = time.time()8chr1 = pr.Data(1)9chr1.read()10read_time = time.time() - start_time11hour, minute, second = pr.time_process(read_time)12print '\n'13print 'Loading time: ' + str(hour) + "h " + str(minute) + "m " + str(second) + "s "14start_time = time.time()15chr1.data_extract(strand_binary=True, pos_normalize=True)16from sklearn import preprocessing17imputer = preprocessing.Imputer(copy=False)18imputer.fit_transform(chr1.train_beta)19process_time = time.time() - start_time20hour, minute, second = pr.time_process(process_time)21print '\n'22print 'Processing time: ' + str(hour) + "h " + str(minute) + "m " + str(second) + "s "23start_time = time.time()24from sklearn import linear_model25predict = []26score = []27fit_alpha = []28train_X = np.transpose(chr1.train_beta[chr1.sample_not_nan,:])29sample_X = chr1.sample_beta[chr1.sample_not_nan]30clf = linear_model.RidgeCV(alphas = alphas)31chr1.regression(clf, train_X, sample_X, predict, score, alpha=fit_alpha)32predict_time = time.time() - start_time33hour, minute, second = pr.time_process(predict_time)34print '\n'35print 'Fitting and Predicting time: ' + str(hour) + "h " + str(minute) + "m " + str(second) + "s "36start_time = time.time()37# Normalized square error for prediction38test_not_nan = []39predict_not_nan = []40true_val = []41err, var= chr1.error_metric(predict, test_not_nan, predict_not_nan, true_val)42print '\n'43print "Number of points:", len(test_not_nan)44print "Var:", var45print "Prediction Error Square:", err46print "Error percentage:", err/var47# Only print out values which have true values48filename = "ridge_regression.txt"49chr1.output(filename, predict_not_nan, predict, true_val, score = score, alpha=fit_alpha)50output_time = time.time() - start_time51hour, minute, second = pr.time_process(output_time)52print '\n'...

lasso_regression.py

Source:lasso_regression.py

1__author__ = 'liangshengzhang'2import process as pr3import numpy as np4import time5start_time = time.time()6chr1 = pr.Data(1)7chr1.read()8read_time = time.time() - start_time9hour, minute, second = pr.time_process(read_time)10print '\n'11print 'Loading time: ' + str(hour) + "h " + str(minute) + "m " + str(second) + "s "12start_time = time.time()13chr1.data_extract(strand_binary=True, pos_normalize=True)14from sklearn import preprocessing15imputer = preprocessing.Imputer(copy=False)16imputer.fit_transform(chr1.train_beta)17process_time = time.time() - start_time18hour, minute, second = pr.time_process(process_time)19print '\n'20print 'Processing time: ' + str(hour) + "h " + str(minute) + "m " + str(second) + "s "21start_time = time.time()22from sklearn import linear_model23predict = []24fit_alpha = []25score = []26train_X = np.transpose(chr1.train_beta[chr1.sample_not_nan,:])27sample_X = chr1.sample_beta[chr1.sample_not_nan]28clf = linear_model.LassoLarsCV(normalize = False, eps=1.0e-7)29chr1.regression(clf, train_X, sample_X, predict, score = score, alpha=fit_alpha)30predict_time = time.time() - start_time31hour, minute, second = pr.time_process(predict_time)32print '\n'33print 'Fitting and Predicting time: ' + str(hour) + "h " + str(minute) + "m " + str(second) + "s "34start_time = time.time()35# Normalized square error for prediction36test_not_nan = []37predict_not_nan = []38true_val = []39err, var= chr1.error_metric(predict, test_not_nan, predict_not_nan, true_val)40print '\n'41print "Number of points:", len(test_not_nan)42print "Var:", var43print "Prediction Error Square:", err44print "Error percentage:", err/var45# Only print out values which have true values46filename = "lasso_regression.txt"47chr1.output(filename, predict_not_nan, predict, true_val, score, alpha=fit_alpha)48output_time = time.time() - start_time49hour, minute, second = pr.time_process(output_time)50print '\n'...

mean.py

Source:mean.py

1__author__ = 'liangshengzhang'2import process as pr3import numpy as np4import time5start_time = time.time()6chr1 = pr.Data(1)7chr1.read()8read_time = time.time() - start_time9hour, minute, second = pr.time_process(read_time)10print '\n'11print 'Loading time: ' + str(hour) + "h " + str(minute) + "m " + str(second) + "s "12start_time = time.time()13chr1.data_extract(strand_binary=True, pos_normalize=True)14from sklearn import preprocessing15imputer = preprocessing.Imputer(copy=False)16imputer.fit_transform(chr1.train_beta)17process_time = time.time() - start_time18hour, minute, second = pr.time_process(process_time)19print '\n'20print 'Processing time: ' + str(hour) + "h " + str(minute) + "m " + str(second) + "s "21train_beta_mean = np.mean(chr1.train_beta,axis=1)22predict = train_beta_mean[chr1.sample_nan]23start_time = time.time()24# Normalized square error for prediction25test_not_nan = []26predict_not_nan = []27true_val = []28err, var= chr1.error_metric(predict, test_not_nan, predict_not_nan, true_val)29print '\n'30print "Number of points:", len(test_not_nan)31print "Var:", var32print "Prediction Error Square:", err33print "Error percentage:", err/var34# Only print out values which have true values35filename = "mean.txt"36chr1.output(filename, predict_not_nan, predict, true_val)37output_time = time.time() - start_time38hour, minute, second = pr.time_process(output_time)39print '\n'...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.