def logistic_gd(df_train, df_test, Y): """ logistic gradient descent """ binary = utils.check_binary(df_train[Y]) model = gd.logistic_gradient(df_train, df_train[Y], 0.1, max_iterations=5) print model predict = gd.predict(df_train, model, binary, True) print predict error_train = mystats.get_error(predict, df_train[Y], binary) predict = gd.predict(df_test, model, binary, True) print predict error_test = mystats.get_error(predict, df_test[Y], binary) return [error_train, error_test]
def linear_gd(df_train, df_test, Y): """ linear gradient descent """ binary = utils.check_binary(df_train[Y]) model = gd.gradient(df_train, df_train[Y], 0.00001, max_iterations=50) print model predict = gd.predict(df_train, model, binary) print predict error_train = mystats.get_error(predict, df_train[Y], binary) predict = gd.predict(df_test, model, binary) print predict error_test = mystats.get_error(predict, df_test[Y], binary) return [error_train, error_test]
def linear_gd_error(df, Y): binary = utils.check_binary(df[Y]) model = gd.gradient(df, df[Y], 0.00001, max_iterations=50) print model predict = gd.predict(df, model, binary) print predict error = mystats.get_error(predict, df_train[Y], binary) return error
def testLogisticGradient(): """ logistic gradient descent """ df_test, df_train = utils.split_test_and_train(utils.load_and_normalize_spam_data()) Y = 'is_spam' binary = utils.check_binary(df_train[Y]) model = gd.logistic_gradient(df_train, df_train[Y], .1, max_iterations=5) #print model #raw_input() predict = gd.predict(df_train, model, binary, True) print predict error_train = mystats.get_error(predict, df_train[Y], binary) #raw_input() predict = gd.predict(df_test, model, binary, True) print predict error_test = mystats.get_error(predict, df_test[Y], binary) print 'error train {} error_test {}'.format(error_train, error_test) return [error_train, error_test]