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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'...

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