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| # --- | ||
| # jupyter: | ||
| # jupytext: | ||
| # formats: r:light | ||
| # text_representation: | ||
| # extension: .r | ||
| # format_name: light | ||
| # format_version: '1.5' | ||
| # jupytext_version: 1.12.0 | ||
| # kernelspec: | ||
| # display_name: R | ||
| # language: R | ||
| # name: ir | ||
| # --- | ||
|
|
||
| library(digest) | ||
| library(testthat) | ||
|
|
||
| test_1.0 <- function() { | ||
| test_that('Did not assign answer to an object called "model_matrix_X_train"', { | ||
| expect_true(exists("model_matrix_X_train")) | ||
| }) | ||
|
|
||
| test_that("Solution should be a matrix", { | ||
| expect_true("matrix" %in% class(model_matrix_X_train)) | ||
| }) | ||
| test_that("Solution should be a matrix", { | ||
| expect_true("matrix" %in% class(matrix_Y_train)) | ||
| }) | ||
|
|
||
| expected_colnames <- c('mean_radius','mean_texture','mean_perimeter','mean_smoothness','mean_compactness','mean_concavity','mean_concave_points','mean_symmetry','mean_fractal_dimension','radius_error','texture_error','perimeter_error','smoothness_error','compactness_error','symmetry_error','fractal_dimension_error') | ||
| given_colnames <- colnames(model_matrix_X_train) | ||
| test_that("Data frame does not have the correct columns", { | ||
| expect_equal(length(setdiff( | ||
| union(expected_colnames, given_colnames), | ||
| intersect(expected_colnames, given_colnames) | ||
| )), 0) | ||
| }) | ||
|
|
||
| test_that("Matrix does not contain the correct number of rows", { | ||
| expect_equal(digest(as.integer(nrow(model_matrix_X_train))), "e1ccdeeda146ea6a2b9098eac7f58ac2") | ||
| }) | ||
| test_that("Matrix does not contain the correct number of rows", { | ||
| expect_equal(digest(as.integer(nrow(matrix_Y_train))), "e1ccdeeda146ea6a2b9098eac7f58ac2") | ||
| }) | ||
|
|
||
| test_that("Matrix does not contain the correct data", { | ||
| expect_equal(digest(as.integer(sum(model_matrix_X_train[,"mean_radius"]) * 10e4)), "da0c890b39f1f7a79777df921f405a41") | ||
| }) | ||
| test_that("Matrix does not contain the correct data", { | ||
| expect_equal(digest(as.integer(sum(matrix_Y_train))), "6ab59a5dc548cdbe65a353f73043f412") | ||
| }) | ||
| print("Success!") | ||
| } | ||
|
|
||
| test_1.1 <- function() { | ||
| test_that('Did not assign answer to an object called "breast_cancer_cv_lambda_ridge"', { | ||
| expect_true(exists("breast_cancer_cv_lambda_ridge")) | ||
| }) | ||
|
|
||
| test_that("Solution should be a cv.glmnet object", { | ||
| expect_true("cv.glmnet" %in% class(breast_cancer_cv_lambda_ridge)) | ||
| }) | ||
|
|
||
| test_that("Data frame does not contain the correct number of rows", { | ||
| expect_equal(digest(breast_cancer_cv_lambda_ridge$index[1,]), "c6df9ff55bfad3fa7254de0d17b5a7f5") | ||
| }) | ||
|
|
||
| test_that("Data frame does not contain the correct data", { | ||
| expect_equal(digest(as.integer(breast_cancer_cv_lambda_ridge$cvm[97]*10e6)), "58664065b5f1854c8e2a89bc43a79959") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } | ||
|
|
||
| test_1.3 <- function() { | ||
| test_that('Did not assign answer to an object called "breast_cancer_lambda_max_AUC_ridge"', { | ||
| expect_true(exists("breast_cancer_lambda_max_AUC_ridge")) | ||
| }) | ||
|
|
||
| answer_as_numeric <- as.numeric(breast_cancer_lambda_max_AUC_ridge) | ||
| test_that("Solution should be a number", { | ||
| expect_false(is.na(answer_as_numeric)) | ||
| }) | ||
|
|
||
| test_that("Solution is incorrect", { | ||
| expect_equal(digest(as.integer(answer_as_numeric * 10e6)), "d40f426836915bf80aad44792e069c0b") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } | ||
|
|
||
| test_1.5 <- function() { | ||
| test_that('Did not assign answer to an object called "breast_cancer_ridge_max_AUC"', { | ||
| expect_true(exists("breast_cancer_ridge_max_AUC")) | ||
| }) | ||
|
|
||
| test_that("Solution should be a glmnet object", { | ||
| expect_true("glmnet" %in% class(breast_cancer_ridge_max_AUC)) | ||
| }) | ||
|
|
||
| test_that("Sultion does not contain the correct number of rows", { | ||
| expect_equal(digest(as.integer(breast_cancer_ridge_max_AUC$lambda*10e3)), "3e58fec15b97b4b65a18dd280f434516") | ||
| }) | ||
|
|
||
| test_that("Solution does not contain the correct data", { | ||
| expect_equal(digest(as.integer(sum(breast_cancer_ridge_max_AUC$beta)*10e5)), "e362f8ba11af909bd5dc45d9642efc7a") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } | ||
|
|
||
| #--------------- | ||
| #deleted from current version | ||
|
|
||
| # test_1.6 <- function() { | ||
| # test_that('Did not assign answer to an object called "breast_cancer_cv_ordinary"', { | ||
| # expect_true(exists("breast_cancer_cv_ordinary")) | ||
| # }) | ||
|
|
||
| # test_that("Solution should be a data frame", { | ||
| # expect_true("cv.glmnet" %in% class(breast_cancer_cv_ordinary)) | ||
| # }) | ||
|
|
||
|
|
||
| # test_that("Solution does not contain the correct number of rows", { | ||
| # expect_equal(digest(as.integer(breast_cancer_cv_ordinary$lambda[2])), "1473d70e5646a26de3c52aa1abd85b1f") | ||
| # }) | ||
|
|
||
| # test_that("Solution does not contain the correct data", { | ||
| # expect_equal(digest(as.integer(breast_cancer_cv_ordinary$cvm[2]*10e6)), "685d8a3a85fdc1b00f0cce6597291ea3") | ||
| # }) | ||
|
|
||
| # print("Success!") | ||
| # } | ||
|
|
||
| #----------- | ||
|
|
||
| test_1.6 <- function() { | ||
| test_that('Did not assign answer to an object called "breast_cancer_AUC_models"', { | ||
| expect_true(exists("breast_cancer_AUC_models")) | ||
| }) | ||
|
|
||
| # test_that("Solution should be a data frame", { | ||
| # expect_true("data.frame" %in% class(breast_cancer_AUC_models)) | ||
| # }) | ||
|
|
||
| expected_colnames <- c("model", "auc") | ||
| given_colnames <- colnames(breast_cancer_AUC_models) | ||
| test_that("Data frame does not have the correct columns", { | ||
| expect_equal(length(setdiff( | ||
| union(expected_colnames, given_colnames), | ||
| intersect(expected_colnames, given_colnames) | ||
| )), 0) | ||
| }) | ||
|
|
||
| test_that("Data frame does not contain the correct number of rows", { | ||
| expect_equal(digest(as.integer(nrow(breast_cancer_AUC_models))), "c01f179e4b57ab8bd9de309e6d576c48") | ||
| }) | ||
|
|
||
| test_that("Data frame does not contain the correct data", { | ||
| expect_equal(digest(as.integer(sum(breast_cancer_AUC_models$auc) * 10e6)), "5631701a7b5ca282c043fe1af5ce9022") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } | ||
|
|
||
| test_1.7 <- function() { | ||
| test_that('Did not assign answer to an object called "breast_cancer_cv_lambda_LASSO"', { | ||
| expect_true(exists("breast_cancer_cv_lambda_LASSO")) | ||
| }) | ||
|
|
||
| test_that("Solution should be a cv.glmnet object", { | ||
| expect_true("cv.glmnet" %in% class(breast_cancer_cv_lambda_LASSO)) | ||
| }) | ||
|
|
||
| test_that("Data frame does not contain the correct number of rows", { | ||
| expect_equal(digest(breast_cancer_cv_lambda_LASSO$index[1,]), "cac17b80df37171f02a533a0962e81ec") | ||
| }) | ||
|
|
||
| test_that("Data frame does not contain the correct data", { | ||
| expect_equal(digest(as.integer(breast_cancer_cv_lambda_LASSO$cvm[97]*10e6)), "c7f66da1cae4f223b9bae717f05900f7") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } | ||
|
|
||
| test_1.8 <- function() { | ||
| test_that('Did not assign answer to an object called "breast_cancer_lambda_1se_AUC_LASSO"', { | ||
| expect_true(exists("breast_cancer_lambda_1se_AUC_LASSO")) | ||
| }) | ||
|
|
||
| answer_as_numeric <- as.numeric(breast_cancer_lambda_1se_AUC_LASSO) | ||
| test_that("Solution should be a number", { | ||
| expect_false(is.na(answer_as_numeric)) | ||
| }) | ||
|
|
||
| test_that("Solution is incorrect", { | ||
| expect_equal(digest(as.integer(answer_as_numeric * 10e6)), "3a2209228b4a81256404f5ad50412e01") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } | ||
|
|
||
| test_1.9 <- function() { | ||
| test_that('Did not assign answer to an object called "breast_cancer_LASSO_1se_AUC"', { | ||
| expect_true(exists("breast_cancer_LASSO_1se_AUC")) | ||
| }) | ||
|
|
||
| test_that("Solution should be a glmnet object", { | ||
| expect_true("glmnet" %in% class(breast_cancer_LASSO_1se_AUC)) | ||
| }) | ||
|
|
||
| test_that("Sultion does not contain the correct number of rows", { | ||
| expect_equal(digest(as.integer(breast_cancer_LASSO_1se_AUC$lambda*10e3)), "4abb356c7b8460ebf96ff801d6539873") | ||
| }) | ||
|
|
||
| test_that("Solution does not contain the correct data", { | ||
| expect_equal(digest(as.integer(sum(breast_cancer_LASSO_1se_AUC$beta)*10e5)), "e8e1e9814f4b16d7df4e4aec6551a55b") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } | ||
|
|
||
| test_1.10 <- function() { | ||
| test_that('Did not assign answer to an object called "answer1.10"', { | ||
| expect_true(exists("answer1.10")) | ||
| }) | ||
|
|
||
| test_that('Solution should be a single character ("A", "B", "C", or "D")', { | ||
| expect_match(answer1.10, "a|b|c|d", ignore.case = TRUE) | ||
| }) | ||
|
|
||
| answer_hash <- digest(tolower(answer1.10)) | ||
|
|
||
| test_that("Solution is incorrect", { | ||
| expect_equal(answer_hash, "f960eee34a9ca222e49c0ae4da40d639") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } | ||
|
|
||
| test_1.11 <- function() { | ||
| test_that('Did not assign answer to an object called "breast_cancer_AUC_models"', { | ||
| expect_true(exists("breast_cancer_AUC_models")) | ||
| }) | ||
|
|
||
| test_that("Solution should be a data frame", { | ||
| expect_true("data.frame" %in% class(breast_cancer_AUC_models)) | ||
| }) | ||
|
|
||
| expected_colnames <- c("model", "auc") | ||
| given_colnames <- colnames(breast_cancer_AUC_models) | ||
| test_that("Data frame does not have the correct columns", { | ||
| expect_equal(length(setdiff( | ||
| union(expected_colnames, given_colnames), | ||
| intersect(expected_colnames, given_colnames) | ||
| )), 0) | ||
| }) | ||
|
|
||
| test_that("Data frame does not contain the correct number of rows", { | ||
| expect_equal(digest(as.integer(nrow(breast_cancer_AUC_models))), "11946e7a3ed5e1776e81c0f0ecd383d0") | ||
| }) | ||
|
|
||
| test_that("Data frame does not contain the correct data", { | ||
| expect_equal(digest(as.integer(sum(breast_cancer_AUC_models$auc) * 10e6)), "6ffe4702e283001ffa5f6625e55c30ed") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } | ||
|
|
||
| test_1.12 <- function() { | ||
| test_that('Did not assign answer to an object called "ROC_lasso"', { | ||
| expect_true(exists("ROC_lasso")) | ||
| }) | ||
|
|
||
|
|
||
| test_that("Data frame does not contain the correct data", { | ||
| expect_equal(digest(as.integer(ROC_lasso$auc * 10e6)), "5521678a8e26ba545b30889d5438dc16") | ||
| }) | ||
|
|
||
| print("Success!") | ||
| } |
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