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2022-11-15 10:53:25 -05:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optional Lab: Model Representation\n",
"\n",
"<figure>\n",
" <img src=\"./images/C1_W1_L3_S1_Lecture_b.png\" style=\"width:600px;height:200px;\">\n",
"</figure>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Goals\n",
"In this lab you will:\n",
"- Learn to implement the model $f_{w,b}$ for linear regression with one variable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notation\n",
"Here is a summary of some of the notation you will encounter. \n",
"\n",
"|General <img width=70/> <br /> Notation <img width=70/> | Description<img width=350/>| Python (if applicable) |\n",
"|: ------------|: ------------------------------------------------------------||\n",
"| $a$ | scalar, non bold ||\n",
"| $\\mathbf{a}$ | vector, bold ||\n",
"| **Regression** | | | |\n",
"| $\\mathbf{x}$ | Training Example feature values (in this lab - Size (1000 sqft)) | `x_train` | \n",
"| $\\mathbf{y}$ | Training Example targets (in this lab Price (1000s of dollars)) | `y_train` \n",
"| $x^{(i)}$, $y^{(i)}$ | $i_{th}$Training Example | `x_i`, `y_i`|\n",
"| m | Number of training examples | `m`|\n",
"| $w$ | parameter: weight | `w` |\n",
"| $b$ | parameter: bias | `b` | \n",
"| $f_{w,b}(x^{(i)})$ | The result of the model evaluation at $x^{(i)}$ parameterized by $w,b$: $f_{w,b}(x^{(i)}) = wx^{(i)}+b$ | `f_wb` | \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tools\n",
"In this lab you will make use of: \n",
"- NumPy, a popular library for scientific computing\n",
"- Matplotlib, a popular library for plotting data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('./deeplearning.mplstyle')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem Statement\n",
"<img align=\"left\" src=\"./images/C1_W1_L3_S1_trainingdata.png\" style=\" width:380px; padding: 10px; \" /> \n",
"\n",
"As in the lecture, you will use the motivating example of housing price prediction. \n",
"This lab will use a simple data set with only two data points - a house with 1000 square feet(sqft) sold for \\\\$300,000 and a house with 2000 square feet sold for \\\\$500,000. These two points will constitute our *data or training set*. In this lab, the units of size are 1000 sqft and the units of price are 1000s of dollars.\n",
"\n",
"| Size (1000 sqft) | Price (1000s of dollars) |\n",
"| -------------------| ------------------------ |\n",
"| 1.0 | 300 |\n",
"| 2.0 | 500 |\n",
"\n",
"You would like to fit a linear regression model (shown above as the blue straight line) through these two points, so you can then predict price for other houses - say, a house with 1200 sqft.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please run the following code cell to create your `x_train` and `y_train` variables. The data is stored in one-dimensional NumPy arrays."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_train = [1. 2.]\n",
"y_train = [300. 500.]\n"
]
}
],
"source": [
"# x_train is the input variable (size in 1000 square feet)\n",
"# y_train is the target (price in 1000s of dollars)\n",
"x_train = np.array([1.0, 2.0])\n",
"y_train = np.array([300.0, 500.0])\n",
"print(f\"x_train = {x_train}\")\n",
"print(f\"y_train = {y_train}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">**Note**: The course will frequently utilize the python 'f-string' output formatting described [here](https://docs.python.org/3/tutorial/inputoutput.html) when printing. The content between the curly braces is evaluated when producing the output."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Number of training examples `m`\n",
"You will use `m` to denote the number of training examples. Numpy arrays have a `.shape` parameter. `x_train.shape` returns a python tuple with an entry for each dimension. `x_train.shape[0]` is the length of the array and number of examples as shown below."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_train.shape: (2,)\n",
"Number of training examples is: 2\n"
]
}
],
"source": [
"# m is the number of training examples\n",
"print(f\"x_train.shape: {x_train.shape}\")\n",
"m = x_train.shape[0]\n",
"print(f\"Number of training examples is: {m}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One can also use the Python `len()` function as shown below."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of training examples is: 2\n"
]
}
],
"source": [
"# m is the number of training examples\n",
"m = len(x_train)\n",
"print(f\"Number of training examples is: {m}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training example `x_i, y_i`\n",
"\n",
"You will use (x$^{(i)}$, y$^{(i)}$) to denote the $i^{th}$ training example. Since Python is zero indexed, (x$^{(0)}$, y$^{(0)}$) is (1.0, 300.0) and (x$^{(1)}$, y$^{(1)}$) is (2.0, 500.0). \n",
"\n",
"To access a value in a Numpy array, one indexes the array with the desired offset. For example the syntax to access location zero of `x_train` is `x_train[0]`.\n",
"Run the next code block below to get the $i^{th}$ training example."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(x^(0), y^(0)) = (1.0, 300.0)\n"
]
}
],
"source": [
"i = 0 # Change this to 1 to see (x^1, y^1)\n",
"\n",
"x_i = x_train[i]\n",
"y_i = y_train[i]\n",
"print(f\"(x^({i}), y^({i})) = ({x_i}, {y_i})\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can plot these two points using the `scatter()` function in the `matplotlib` library, as shown in the cell below. \n",
"- The function arguments `marker` and `c` show the points as red crosses (the default is blue dots).\n",
"\n",
"You can use other functions in the `matplotlib` library to set the title and labels to display"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the data points\n",
"plt.scatter(x_train, y_train, marker='x', c='r')\n",
"# Set the title\n",
"plt.title(\"Housing Prices\")\n",
"# Set the y-axis label\n",
"plt.ylabel('Price (in 1000s of dollars)')\n",
"# Set the x-axis label\n",
"plt.xlabel('Size (1000 sqft)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model function\n",
"\n",
"<img align=\"left\" src=\"./images/C1_W1_L3_S1_model.png\" style=\" width:380px; padding: 10px; \" > As described in lecture, the model function for linear regression (which is a function that maps from `x` to `y`) is represented as \n",
"\n",
"$$ f_{w,b}(x^{(i)}) = wx^{(i)} + b \\tag{1}$$\n",
"\n",
"The formula above is how you can represent straight lines - different values of $w$ and $b$ give you different straight lines on the plot. <br/> <br/> <br/> <br/> <br/> \n",
"\n",
"Let's try to get a better intuition for this through the code blocks below. Let's start with $w = 100$ and $b = 100$. \n",
"\n",
"**Note: You can come back to this cell to adjust the model's w and b parameters**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w: 100\n",
"b: 100\n"
]
}
],
"source": [
"w = 100\n",
"b = 100\n",
"print(f\"w: {w}\")\n",
"print(f\"b: {b}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's compute the value of $f_{w,b}(x^{(i)})$ for your two data points. You can explicitly write this out for each data point as - \n",
"\n",
"for $x^{(0)}$, `f_wb = w * x[0] + b`\n",
"\n",
"for $x^{(1)}$, `f_wb = w * x[1] + b`\n",
"\n",
"For a large number of data points, this can get unwieldy and repetitive. So instead, you can calculate the function output in a `for` loop as shown in the `compute_model_output` function below.\n",
"> **Note**: The argument description `(ndarray (m,))` describes a Numpy n-dimensional array of shape (m,). `(scalar)` describes an argument without dimensions, just a magnitude. \n",
"> **Note**: `np.zero(n)` will return a one-dimensional numpy array with $n$ entries \n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def compute_model_output(x, w, b):\n",
" \"\"\"\n",
" Computes the prediction of a linear model\n",
" Args:\n",
" x (ndarray (m,)): Data, m examples \n",
" w,b (scalar) : model parameters \n",
" Returns\n",
" y (ndarray (m,)): target values\n",
" \"\"\"\n",
" m = x.shape[0]\n",
" f_wb = np.zeros(m)\n",
" for i in range(m):\n",
" f_wb[i] = w * x[i] + b\n",
" \n",
" return f_wb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's call the `compute_model_output` function and plot the output.."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tmp_f_wb = compute_model_output(x_train, w, b,)\n",
"\n",
"# Plot our model prediction\n",
"plt.plot(x_train, tmp_f_wb, c='b',label='Our Prediction')\n",
"\n",
"# Plot the data points\n",
"plt.scatter(x_train, y_train, marker='x', c='r',label='Actual Values')\n",
"\n",
"# Set the title\n",
"plt.title(\"Housing Prices\")\n",
"# Set the y-axis label\n",
"plt.ylabel('Price (in 1000s of dollars)')\n",
"# Set the x-axis label\n",
"plt.xlabel('Size (1000 sqft)')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, setting $w = 100$ and $b = 100$ does *not* result in a line that fits our data. \n",
"\n",
"### Challenge\n",
"Try experimenting with different values of $w$ and $b$. What should the values be for a line that fits our data?\n",
"\n",
"#### Tip:\n",
"You can use your mouse to click on the green \"Hints\" below to reveal some hints for choosing b and w."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details>\n",
"<summary>\n",
" <font size='3', color='darkgreen'><b>Hints</b></font>\n",
"</summary>\n",
" <p>\n",
" <ul>\n",
" <li>Try $w = 200$ and $b = 100$ </li>\n",
" </ul>\n",
" </p>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prediction\n",
"Now that we have a model, we can use it to make our original prediction. Let's predict the price of a house with 1200 sqft. Since the units of $x$ are in 1000's of sqft, $x$ is 1.2.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"$340 thousand dollars\n"
]
}
],
"source": [
"w = 200 \n",
"b = 100 \n",
"x_i = 1.2\n",
"cost_1200sqft = w * x_i + b \n",
"\n",
"print(f\"${cost_1200sqft:.0f} thousand dollars\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Congratulations!\n",
"In this lab you have learned:\n",
" - Linear regression builds a model which establishes a relationship between features and targets\n",
" - In the example above, the feature was house size and the target was house price\n",
" - for simple linear regression, the model has two parameters $w$ and $b$ whose values are 'fit' using *training data*.\n",
" - once a model's parameters have been determined, the model can be used to make predictions on novel data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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