02 - What can go wrong and how to fix it.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "## Python object vs nopython mode\n",
    "A lof of times, we will run into compilation problem that we will never exepriecne with Numba. It is because, in pure Python we do not complie any code. Here is an example of how we can run into a compilation error when using nopython mode\n",
    "\n",
    "First consider the code below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adding_numbers(x, y):\n",
    "    return x + y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a simple function adding two numbers so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adding_numbers(1,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "would gives you 3, let's try change the first input into a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adding_numbers(\"1\",2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Would have an error as we are trying to add a string to an integer.\n",
    "\n",
    "**Exercise: change the code in \"adding_numbers\" to \"fix\" the above**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise: use `njit` on the \"fixed\" function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Un-commend the line below to see how we do it.\n",
    "#%load adding_numbers.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So f is compiled with nopython mode (you can use `@njit` or `@jit(nopython=True)`, try changing things up to see it yourself). It has no problem if we use it with integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adding_numbers(1,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try change the first input into a string. Since we convert the imputs to ints it sould work... right?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adding_numbers(\"1\",2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a compilation error that we would never see when using pure Python. This is an error that came from Numba. From the Traceback we see there is an error message that mension TypingError and it's something to do with the nopython mode pipeline.\n",
    "\n",
    "So let's try to make Python object avaliable.\n",
    "\n",
    "**Exercise: rename \"adding_numbers\" to \"adding_numbers_again\" and use `jit` on it**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Un-commend the line below to see how we do it.\n",
    "#%load adding_numbers_again.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we use the object mode which allow the use of the Python interpretor and we will see what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adding_numbers_again(\"1\",2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we see that the function does compiled, we ony get a warning from Numba, which says we are now using object mode. (so it will be slower than expected, see **Extra Task** below)\n",
    "\n",
    "Now it is your turn to try.\n",
    "\n",
    "Let's say we have a function that will add one to a number if it is not reaching 10 yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def adding_one(x):\n",
    "    if x < 10:\n",
    "        return x+1\n",
    "    else:\n",
    "        return \"too big\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adding_one(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is quite fustriting as we cannot compile this function, it is due to Numba not able to  determine the return type of a function for compilation. Maybe the Python interpreter can help?\n",
    "\n",
    "**Exercise: use the proper decorator for the function below**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## whcih decorator will you use?\n",
    "def adding_one_again(x):\n",
    "    if x < 10:\n",
    "        return x+1\n",
    "    else:\n",
    "        return \"too big\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try to see if it works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adding_one_again(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Extra Task: profile `adding_numbers_again` with integers and with strings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%timeit\n",
    "adding_numbers_again(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%timeit\n",
    "adding_numbers_again(\"1\",\"2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another things that Numba don't like during compiling in nopython mode is empty list. The type of the items in the list is infered when there are things inside. So this is absolutely fine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def count_to_three():\n",
    "    tmp = [1,2,3]\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_to_three()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But when we have an empty list, Numba doesn't know what kind of things you will put into the list. So we will have trouble doing this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def give_me_empty():\n",
    "    tmp = []\n",
    "    return tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "give_me_empty()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, can you figure it out a way to return an empty list using `njit`? We have to somehow state the type of the items in the list without putting any items in the list...\n",
    "\n",
    "**Exercise: try to \"fix\" the above**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Un-commend the line below to see how we do it.\n",
    "#%load give_me_empty.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "give_me_empty()"
   ]
  }
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