|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "metadata": { |
| 6 | + "slideshow": { |
| 7 | + "slide_type": "slide" |
| 8 | + } |
| 9 | + }, |
| 10 | + "source": [ |
| 11 | + "# Testing\n", |
| 12 | + "\n", |
| 13 | + "We now have a fully automated script! 🎉👏🏻🦄\n", |
| 14 | + "\n", |
| 15 | + "The next step is to include **tests**... in fact testing should be a core part of our development process. In fact all of our **reproducible workflows** are analogous to experimental design in the scientific world\n", |
| 16 | + "\n", |
| 17 | + "\n", |
| 18 | + "\n", |
| 19 | + "<small> https://xkcd.com/242/ </small>" |
| 20 | + ] |
| 21 | + }, |
| 22 | + { |
| 23 | + "cell_type": "markdown", |
| 24 | + "metadata": {}, |
| 25 | + "source": [ |
| 26 | + "There are various approaches to tests software:\n", |
| 27 | + "- Assertions\n", |
| 28 | + "- Exceptions: within the code serve as ⚠️\n", |
| 29 | + "- Unit tests: investigate the behaviour of units of code (e.g functions)\n", |
| 30 | + "- Regression tests: defends against 🐛\n", |
| 31 | + "- Integration tests: ⚙️ checks that the pieces work together as expected" |
| 32 | + ] |
| 33 | + }, |
| 34 | + { |
| 35 | + "cell_type": "markdown", |
| 36 | + "metadata": {}, |
| 37 | + "source": [ |
| 38 | + "We will start by testing some of our functions:\n", |
| 39 | + "Open `03_country-subset.py` and add the following function:\n", |
| 40 | + " \n", |
| 41 | + "```python \n", |
| 42 | + "def get_mean_price(filename):\n", |
| 43 | + " \"\"\" function to get the mean price of the wines\n", |
| 44 | + " rounded to 4 decimals\"\"\"\n", |
| 45 | + " wine = pd.read_csv(filename)\n", |
| 46 | + " mean_price = wine['price'].mean()\n", |
| 47 | + " return round(mean_price, 4)\n", |
| 48 | + "```\n", |
| 49 | + "\n", |
| 50 | + "And we will modify this function too:\n", |
| 51 | + "```python\n", |
| 52 | + "def get_country(filename, country):\n", |
| 53 | + " \n", |
| 54 | + "\n", |
| 55 | + " # Load table\n", |
| 56 | + " wine = pd.read_csv(filename)\n", |
| 57 | + "\n", |
| 58 | + " # Use the country name to subset data\n", |
| 59 | + " subset_country = wine[wine['country'] == country ].copy()\n", |
| 60 | + "\n", |
| 61 | + " # Subset the\n", |
| 62 | + "\n", |
| 63 | + " # Constructing the fname\n", |
| 64 | + " today = datetime.datetime.today().strftime('%Y-%m-%d')\n", |
| 65 | + " fname = f'data/processed/{today}-winemag_{country}.csv'\n", |
| 66 | + "\n", |
| 67 | + " # Saving the csv\n", |
| 68 | + " subset_country.to_csv(fname)\n", |
| 69 | + " print(fname) # print the fname from here\n", |
| 70 | + "\n", |
| 71 | + " return(subset_country) #returns the data frame\n", |
| 72 | + "```" |
| 73 | + ] |
| 74 | + }, |
| 75 | + { |
| 76 | + "cell_type": "markdown", |
| 77 | + "metadata": {}, |
| 78 | + "source": [ |
| 79 | + "Now we need to create out testing scripts. \n", |
| 80 | + "Some resources:\n", |
| 81 | + "- Pytest usage examples can be found [here](http://doc.pytest.org/en/latest/usage.html)\n", |
| 82 | + "- Rules for [test discovery](http://doc.pytest.org/en/latest/goodpractices.html)\n", |
| 83 | + "\n", |
| 84 | + "Now we can create our tests:\n", |
| 85 | + "```\n", |
| 86 | + "$ touch tests/__init__.py\n", |
| 87 | + "$ touch test_03_country_subset.py\n", |
| 88 | + "```\n", |
| 89 | + "Your test scrips should start with `test`" |
| 90 | + ] |
| 91 | + }, |
| 92 | + { |
| 93 | + "cell_type": "markdown", |
| 94 | + "metadata": {}, |
| 95 | + "source": [ |
| 96 | + "Your test script should look like this:\n", |
| 97 | + "``` python\n", |
| 98 | + "import importlib\n", |
| 99 | + "\n", |
| 100 | + "country = importlib.import_module('.data.03_country-subset', 'src')\n", |
| 101 | + "\n", |
| 102 | + "interim_data = \"data/interim/2018-04-30-winemag_priceGBP.csv\"\n", |
| 103 | + "processed_data = \"data/processed/2018-04-30-winemag_Chile.csv\"\n", |
| 104 | + "\n", |
| 105 | + "def test_get_mean_price():\n", |
| 106 | + " mean_price = country.get_mean_price(processed_data)\n", |
| 107 | + " assert mean_price == 20.7865\n", |
| 108 | + "```\n", |
| 109 | + "\n", |
| 110 | + "And you can run it from the shell using:\n", |
| 111 | + "```\n", |
| 112 | + "$ python -m pytest tests/test_03_country-subset.py\n", |
| 113 | + "```" |
| 114 | + ] |
| 115 | + }, |
| 116 | + { |
| 117 | + "cell_type": "markdown", |
| 118 | + "metadata": {}, |
| 119 | + "source": [ |
| 120 | + "## What if you want all the decimal numbers?\n", |
| 121 | + "\n", |
| 122 | + "``` python\n", |
| 123 | + "import importlib\n", |
| 124 | + "import numpy.testing as npt\n", |
| 125 | + "\n", |
| 126 | + "country = importlib.import_module('.data.03_country-subset', 'src')\n", |
| 127 | + "\n", |
| 128 | + "interim_data = \"data/interim/2018-04-30-winemag_priceGBP.csv\"\n", |
| 129 | + "processed_data = \"data/processed/2018-04-30-winemag_Chile.csv\"\n", |
| 130 | + "\n", |
| 131 | + "def test_get_mean_price():\n", |
| 132 | + " mean_price = country.get_mean_price(processed_data)\n", |
| 133 | + " assert mean_price == 20.7865\n", |
| 134 | + " npt.assert_allclose(country.get_mean_price(processed_data) , 20.787, rtol = 0.01)\n", |
| 135 | + "```\n", |
| 136 | + "\n", |
| 137 | + "The `numpy.testing.assert_allclose` allows you to set a tolerance " |
| 138 | + ] |
| 139 | + }, |
| 140 | + { |
| 141 | + "cell_type": "markdown", |
| 142 | + "metadata": {}, |
| 143 | + "source": [ |
| 144 | + "### What else could go wrong?\n", |
| 145 | + "\n", |
| 146 | + "What if we created a data set and we want to make sure that my interim or raw data has not changed? -> Thus my dataframes have not changes either?\n", |
| 147 | + "\n", |
| 148 | + "```python \n", |
| 149 | + "import pandas.testing as pdt\n", |
| 150 | + "import pandas as pd\n", |
| 151 | + "\n", |
| 152 | + "\n", |
| 153 | + "interim_data = \"data/interim/2018-05-09-winemag_priceGBP.csv\"\n", |
| 154 | + "processed_data = \"data/processed/2018-05-09-winemag_Chile.csv\"\n", |
| 155 | + "\n", |
| 156 | + "def test_get_country():\n", |
| 157 | + " # call the function\n", |
| 158 | + " df = country.get_country(interim_data, 'Chile')\n", |
| 159 | + " \n", |
| 160 | + " # load my previous dataset\n", |
| 161 | + " base = pd.read_csv(processed_data)\n", |
| 162 | + " \n", |
| 163 | + " # check if I am getting a dataframe\n", |
| 164 | + " assert isinstance(df, pd.DataFrame)\n", |
| 165 | + " assert isinstance(base, pd.DataFrame)\n", |
| 166 | + " \n", |
| 167 | + " # check that they are the same dataframes\n", |
| 168 | + " pdt.assert_frame_equal(df, base)\n", |
| 169 | + "``` " |
| 170 | + ] |
| 171 | + }, |
| 172 | + { |
| 173 | + "cell_type": "markdown", |
| 174 | + "metadata": {}, |
| 175 | + "source": [ |
| 176 | + "### See what we did in the previous steps?\n", |
| 177 | + "\n", |
| 178 | + "We tested each of the functions in our module...\n", |
| 179 | + "we did *unit testing*!\n", |
| 180 | + "Notice something in the functions we just wrote? \n", |
| 181 | + "- Set-up: `mean = country.get_mean(interim_data)`\n", |
| 182 | + "- Assertions: `assert mean_price == 20.786`" |
| 183 | + ] |
| 184 | + }, |
| 185 | + { |
| 186 | + "cell_type": "code", |
| 187 | + "execution_count": 1, |
| 188 | + "metadata": {}, |
| 189 | + "outputs": [ |
| 190 | + { |
| 191 | + "data": { |
| 192 | + "text/html": [ |
| 193 | + "<link href=\"https://fonts.googleapis.com/css?family=Didact+Gothic|Dosis:400,500,700\" rel=\"stylesheet\"><style>\n", |
| 194 | + "@font-face {\n", |
| 195 | + " font-family: \"Computer Modern\";\n", |
| 196 | + " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", |
| 197 | + "}\n", |
| 198 | + "/* div.cell{\n", |
| 199 | + "width:800px;\n", |
| 200 | + "margin-left:16% !important;\n", |
| 201 | + "margin-right:auto;\n", |
| 202 | + "} */\n", |
| 203 | + "h1 {\n", |
| 204 | + " font-family: 'Dosis', \"Helvetica Neue\", Arial, sans-serif;\n", |
| 205 | + " color: #0B132B;\n", |
| 206 | + "}\n", |
| 207 | + "h2 {\n", |
| 208 | + " font-family: 'Dosis', sans-serif;\n", |
| 209 | + " color: #1C2541;\n", |
| 210 | + "}\n", |
| 211 | + "h3{\n", |
| 212 | + " font-family: 'Dosis', sans-serif;\n", |
| 213 | + " margin-top:12px;\n", |
| 214 | + " margin-bottom: 3px;\n", |
| 215 | + " color: #40a8a6;\n", |
| 216 | + "}\n", |
| 217 | + "h4{\n", |
| 218 | + " font-family: 'Dosis', sans-serif;\n", |
| 219 | + " color: #40a8a6;\n", |
| 220 | + "}\n", |
| 221 | + "h5 {\n", |
| 222 | + " font-family: 'Dosis', sans-serif;\n", |
| 223 | + " color: #40a8a6;\n", |
| 224 | + "}\n", |
| 225 | + "div.text_cell_render{\n", |
| 226 | + " font-family: 'Didact Gothic',Computer Modern, \"Helvetica Neue\", Arial, Helvetica,\n", |
| 227 | + " Geneva, sans-serif;\n", |
| 228 | + " line-height: 130%;\n", |
| 229 | + " font-size: 110%;\n", |
| 230 | + " /* width:600px; */\n", |
| 231 | + " /* margin-left:auto;\n", |
| 232 | + " margin-right:auto; */\n", |
| 233 | + "}\n", |
| 234 | + "\n", |
| 235 | + ".text_cell_render h1 {\n", |
| 236 | + " font-weight: 200;\n", |
| 237 | + " font-size: 30pt;\n", |
| 238 | + " /* font-size: 50pt */\n", |
| 239 | + " line-height: 100%;\n", |
| 240 | + " color:#0B132B;\n", |
| 241 | + " margin-bottom: 0.5em;\n", |
| 242 | + " margin-top: 0.5em;\n", |
| 243 | + " display: block;\n", |
| 244 | + "}\n", |
| 245 | + "\n", |
| 246 | + ".text_cell_render h2{\n", |
| 247 | + " font-weight: 500;\n", |
| 248 | + "}\n", |
| 249 | + "\n", |
| 250 | + ".text_cell_render h3{\n", |
| 251 | + " font-weight: 500;\n", |
| 252 | + "}\n", |
| 253 | + "\n", |
| 254 | + "\n", |
| 255 | + ".warning{\n", |
| 256 | + " color: rgb( 240, 20, 20 )\n", |
| 257 | + "}\n", |
| 258 | + "\n", |
| 259 | + "div.warn {\n", |
| 260 | + " background-color: #FF5A5F;\n", |
| 261 | + " border-color: #FF5A5F;\n", |
| 262 | + " border-left: 5px solid #C81D25;\n", |
| 263 | + " padding: 0.5em;\n", |
| 264 | + "\n", |
| 265 | + " color: #fff;\n", |
| 266 | + " opacity: 0.8;\n", |
| 267 | + "}\n", |
| 268 | + "\n", |
| 269 | + "div.info {\n", |
| 270 | + " background-color: #087E8B;\n", |
| 271 | + " border-color: #087E8B;\n", |
| 272 | + " border-left: 5px solid #0B3954;\n", |
| 273 | + " padding: 0.5em;\n", |
| 274 | + " color: #fff;\n", |
| 275 | + " opacity: 0.8;\n", |
| 276 | + "}\n", |
| 277 | + "\n", |
| 278 | + "</style>\n", |
| 279 | + "<script>\n", |
| 280 | + "MathJax.Hub.Config({\n", |
| 281 | + " TeX: {\n", |
| 282 | + " extensions: [\"AMSmath.js\"]\n", |
| 283 | + " },\n", |
| 284 | + " tex2jax: {\n", |
| 285 | + " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", |
| 286 | + " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", |
| 287 | + " },\n", |
| 288 | + " displayAlign: 'center', // Change this to 'center' to center equations.\n", |
| 289 | + " \"HTML-CSS\": {\n", |
| 290 | + " styles: {'.MathJax_Display': {\"margin\": 4}}\n", |
| 291 | + " }\n", |
| 292 | + " });\n", |
| 293 | + " </script>\n" |
| 294 | + ], |
| 295 | + "text/plain": [ |
| 296 | + "<IPython.core.display.HTML object>" |
| 297 | + ] |
| 298 | + }, |
| 299 | + "execution_count": 1, |
| 300 | + "metadata": {}, |
| 301 | + "output_type": "execute_result" |
| 302 | + } |
| 303 | + ], |
| 304 | + "source": [ |
| 305 | + "from IPython.core.display import HTML\n", |
| 306 | + "\n", |
| 307 | + "\n", |
| 308 | + "def css_styling():\n", |
| 309 | + " styles = open(\"styles/custom.css\", \"r\").read()\n", |
| 310 | + " return HTML(styles)\n", |
| 311 | + "css_styling()" |
| 312 | + ] |
| 313 | + } |
| 314 | + ], |
| 315 | + "metadata": { |
| 316 | + "kernelspec": { |
| 317 | + "display_name": "Python 3", |
| 318 | + "language": "python", |
| 319 | + "name": "python3" |
| 320 | + }, |
| 321 | + "language_info": { |
| 322 | + "codemirror_mode": { |
| 323 | + "name": "ipython", |
| 324 | + "version": 3 |
| 325 | + }, |
| 326 | + "file_extension": ".py", |
| 327 | + "mimetype": "text/x-python", |
| 328 | + "name": "python", |
| 329 | + "nbconvert_exporter": "python", |
| 330 | + "pygments_lexer": "ipython3", |
| 331 | + "version": "3.6.5" |
| 332 | + } |
| 333 | + }, |
| 334 | + "nbformat": 4, |
| 335 | + "nbformat_minor": 2 |
| 336 | +} |
0 commit comments