{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 第六章 社会科学家的机器学习"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"\n",
"\n",
"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
"\n",
"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Python machine learning \n",
"\n",
"- [Scikit-Learn](http://scikit-learn.org) provides efficient versions of a large number of common algorithms.\n",
" - Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation.\n",
"\n",
"Once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Python machine learning \n",
"\n",
"A solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches.\n",
"\n",
"This section provides an overview of the Scikit-Learn API \n",
"\n",
"- The *data representation* in Scikit-Learn\n",
"- The *Estimator* API\n",
" - a more interesting example of using these tools for exploring a set of images of hand-written digits."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Data Representation in Scikit-Learn"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Machine learning is about creating models from data: \n",
"- How data can be represented in order to be understood by the computer.\n",
" - Tables of data."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Data as table\n",
"\n",
"A basic table is a two-dimensional grid of data\n",
"- the rows represent individual elements of the dataset\n",
"- the columns represent quantities related to each of these elements.\n",
"\n",
"For example, consider the [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), famously analyzed by Ronald Fisher in 1936.\n",
"\n",
"We can download this dataset in the form of a Pandas ``DataFrame`` using the [seaborn](http://seaborn.pydata.org/) library:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T01:51:00.007026Z",
"start_time": "2021-05-21T01:50:57.697149Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
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" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
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},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import seaborn as sns\n",
"sns.set_context(\"talk\", font_scale=1.5)\n",
"\n",
"iris = sns.load_dataset('iris')\n",
"iris.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Each row of the data refers to a single observed flower\n",
"- The number of rows is the total number of flowers in the dataset.\n",
" - the rows of the matrix as *samples*\n",
" - the number of rows as ``n_samples``.\n",
"- each column of the data refers to a particular quantitative piece of information that describes each sample.\n",
" - the columns of the matrix as *features*\n",
" - the number of columns as ``n_features``."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Features matrix\n",
"\n",
"This table layout of the information can be thought of as a ``two-dimensional numerical array or matrix``, which we will call the **features matrix**.\n",
"\n",
"- The features matrix is often stored in a variable named ``X``.\n",
"- The features matrix is assumed to be two-dimensional, with shape ``[n_samples, n_features]``, \n",
"- The features matrix is most often contained in a NumPy array or a Pandas ``DataFrame``\n",
"- some Scikit-Learn models also accept SciPy sparse matrices.\n",
"\n",
"The samples (i.e., rows) always refer to the individual objects described by the dataset.\n",
"- For example, the sample might be a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements.\n",
"\n",
"The features (i.e., columns) always refer to the distinct observations that describe each sample in a quantitative manner.\n",
"- Features are generally real-valued, but may be Boolean or discrete-valued in some cases."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Target array\n",
"\n",
"In addition to the feature matrix ``X``, we also generally work with a *label* or *target* array, which by convention we will usually call ``y``.\n",
"- The target array is usually one dimensional, with length ``n_samples``\n",
"- The target array is generally contained in a NumPy array or Pandas ``Series``.\n",
"- The target array may have continuous numerical values, or discrete classes/labels.\n",
"\n",
"While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, ``[n_samples, n_targets]`` target array, we will primarily be working with the common case of a one-dimensional target array."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Target array\n",
"\n",
"The target array is usually the quantity we want to *predict from the data*: in statistical terms, it is the dependent variable.\n",
"> For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the target array.\n",
"\n",
"With this target array in mind, we can use Seaborn to conveniently visualize the data:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-15T12:51:31.747920Z",
"start_time": "2018-05-15T12:51:26.463393Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import seaborn as sns; \n",
"sns.set()\n",
"sns.set_context(\"talk\", font_scale=1)\n",
"sns.pairplot(iris, hue='species', height=2);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``\n",
"- we can use some of the Pandas ``DataFrame`` operations."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T01:53:24.244139Z",
"start_time": "2021-05-21T01:53:24.236209Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(150, 4)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_iris = iris.drop('species', axis=1)\n",
"X_iris.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T01:53:35.091019Z",
"start_time": "2021-05-21T01:53:35.082668Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
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"\n",
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" \n",
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" \n",
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"text/plain": [
" sepal_length sepal_width petal_length petal_width\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_iris[:3]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T01:53:51.990823Z",
"start_time": "2021-05-21T01:53:51.985623Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(150,)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_iris = iris['species']\n",
"y_iris.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-15T07:06:05.380136Z",
"start_time": "2018-05-15T07:06:05.374539Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0 setosa\n",
"1 setosa\n",
"2 setosa\n",
"Name: species, dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_iris[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"To summarize, the expected layout of features and target values is visualized in the following diagram:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Scikit-Learn's Estimator API"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Guiding principles outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n",
"\n",
"- *Consistency*: All objects share a common interface drawn from a limited set of methods, with consistent documentation.\n",
" - Every machine learning algorithm in Scikit-Learn is implemented via the Estimator API, which provides a consistent interface for a wide range of machine learning applications.\n",
"\n",
"- *Inspection*: All specified parameter values are exposed as public attributes.\n",
"\n",
"- *Limited object hierarchy*: \n",
" - Only algorithms are represented by Python classes; \n",
" - datasets are represented in standard formats (NumPy arrays, Pandas ``DataFrame``s, SciPy sparse matrices) and \n",
" - parameter names use standard Python strings.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Guiding principles outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n",
"\n",
"- *Composition*: Many machine learning tasks can be expressed as sequences of more fundamental algorithms,\n",
" and Scikit-Learn makes use of this wherever possible.\n",
"\n",
"- *Sensible defaults*: When models require user-specified parameters, the library defines an appropriate default value.\n",
"\n",
"> In practice, these principles make Scikit-Learn very easy to use, once the basic principles are understood.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Basics of the API\n",
"\n",
"1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn.\n",
"2. Choose model hyperparameters by instantiating this class with desired values.\n",
"3. Arrange data into a features matrix and target vector following the discussion above.\n",
"4. Fit the model to your data by calling the ``fit()`` method of the model instance.\n",
"5. Apply the Model to new data:\n",
" - For supervised learning, often we predict labels for unknown data using the ``predict()`` method.\n",
" - For unsupervised learning, we often transform or infer properties of the data using the ``transform()`` or ``predict()`` method.\n",
"\n",
"We will now step through several simple examples of applying supervised and unsupervised learning methods."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Supervised learning example: Simple linear regression\n",
"\n",
"As an example of this process, let's consider a simple linear regression—that is, the common case of fitting a line to $(x, y)$ data.\n",
"We will use the following simple data for our regression example:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:01:57.789885Z",
"start_time": "2021-05-21T02:01:57.613025Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"rng = np.random.RandomState(42)\n",
"x = 10 * rng.rand(50)\n",
"y = 2 * x - 1 + rng.randn(50)\n",
"plt.scatter(x, y)\n",
"plt.xlabel('x', fontsize = 30)\n",
"plt.ylabel('y', fontsize = 30);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"With this data in place, we can use the recipe outlined earlier. Let's walk through the process: "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### 1. Choose a class of model\n",
"\n",
"In Scikit-Learn, every class of model is represented by a Python class.\n",
"So, for example, if we would like to compute a simple linear regression model, we can import the linear regression class:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:03:15.363663Z",
"start_time": "2021-05-21T02:03:15.168882Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### 2. Choose model hyperparameters\n",
"\n",
"An important point is that *a class of model is not the same as an instance of a model*.\n",
"\n",
"Once we have decided on our model class, there are still some options open to us.\n",
"Depending on the model class we are working with, we might need to answer one or more questions like the following:\n",
"\n",
"- Would we like to fit for the offset (i.e., *y*-intercept)?\n",
"- Would we like the model to be normalized?\n",
"- Would we like to preprocess our features to add model flexibility?\n",
"- What degree of regularization would we like to use in our model?\n",
"- How many model components would we like to use?\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### 2. Choose model hyperparameters\n",
"\n",
"\n",
"These are examples of the important choices that must be made *once the model class is selected*.\n",
"- These choices are often represented as *hyperparameters*, or parameters that must be set before the model is fit to data.\n",
"- In Scikit-Learn, hyperparameters are chosen by passing values at model instantiation.\n",
"\n",
"We will explore how you can quantitatively motivate the choice of hyperparameters in **Hyperparameters and Model Validation**.\n",
"\n",
"For our linear regression example, we can instantiate the ``LinearRegression`` class and specify that we would like to fit the intercept using the ``fit_intercept`` hyperparameter:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:04:04.752836Z",
"start_time": "2021-05-21T02:04:04.745934Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = LinearRegression(fit_intercept=True)\n",
"model\n",
"#help(LinearRegression)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values.\n",
"- In particular, we have not yet applied the model to any data: \n",
"- the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### 3. Arrange data into a features matrix and target vector\n",
"\n",
"Previously we detailed the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array.\n",
"\n",
"- The target variable ``y`` is already in the correct form (a length-``n_samples`` array)\n",
"- The feature matrix ``x`` should be transformed to a matrix of size ``[n_samples, n_features]``.\n",
"\n",
"In this case, this amounts to a simple reshaping of the one-dimensional array:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:06:28.093321Z",
"start_time": "2021-05-21T02:06:28.088662Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[3.74540119],\n",
" [9.50714306],\n",
" [7.31993942]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = x[:, np.newaxis]\n",
"X[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### 4. Fit the model to your data\n",
"\n",
"Now it is time to apply our model to data.\n",
"This can be done with the ``fit()`` method of the model:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:07:16.022370Z",
"start_time": "2021-05-21T02:07:15.994448Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n",
"\n",
"In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; \n",
"- for example in this linear model, we have the following:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:07:37.003681Z",
"start_time": "2021-05-21T02:07:37.000266Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([1.9776566])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The parameters represent the slope of the simple linear fit to the data.\n",
"model.coef_"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-15T07:07:28.062749Z",
"start_time": "2018-05-15T07:07:28.058403Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"-0.9033107255311164"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The parameter represent the intercept of the simple linear fit to the data.\n",
"model.intercept_ "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1.\n",
"\n",
"One question that frequently comes up regards the uncertainty in such internal model parameters.\n",
"\n",
"In general, Scikit-Learn does not provide tools to draw conclusions from internal model parameters themselves:\n",
"- interpreting model parameters is much more a *statistical modeling* question than a *machine learning* question.\n",
"- Machine learning rather focuses on what the model *predicts*.\n",
"\n",
"If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [Statsmodels Python package](http://statsmodels.sourceforge.net/)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### 5. Predict labels for unknown data\n",
"\n",
"Once the model is trained, the main task of supervised machine learning is to evaluate it based on what it says about new data that was not part of the training set.\n",
"In Scikit-Learn, this can be done using the ``predict()`` method.\n",
"For the sake of this example, our \"new data\" will be a grid of *x* values, and we will ask what *y* values the model predicts:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:08:59.242336Z",
"start_time": "2021-05-21T02:08:59.237707Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([-1. , -0.75510204, -0.51020408, -0.26530612, -0.02040816,\n",
" 0.2244898 , 0.46938776, 0.71428571, 0.95918367, 1.20408163,\n",
" 1.44897959, 1.69387755, 1.93877551, 2.18367347, 2.42857143,\n",
" 2.67346939, 2.91836735, 3.16326531, 3.40816327, 3.65306122,\n",
" 3.89795918, 4.14285714, 4.3877551 , 4.63265306, 4.87755102,\n",
" 5.12244898, 5.36734694, 5.6122449 , 5.85714286, 6.10204082,\n",
" 6.34693878, 6.59183673, 6.83673469, 7.08163265, 7.32653061,\n",
" 7.57142857, 7.81632653, 8.06122449, 8.30612245, 8.55102041,\n",
" 8.79591837, 9.04081633, 9.28571429, 9.53061224, 9.7755102 ,\n",
" 10.02040816, 10.26530612, 10.51020408, 10.75510204, 11. ])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xfit = np.linspace(-1, 11)\n",
"xfit"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:10:58.081994Z",
"start_time": "2021-05-21T02:10:58.078058Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"Xfit = xfit[:, np.newaxis]\n",
"ytest = 2*Xfit -1 \n",
"yfit = model.predict(Xfit)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Finally, let's visualize the results by plotting first the raw data, and then this model fit:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:12:09.277502Z",
"start_time": "2021-05-21T02:12:09.272341Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.9998706727838067"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import r2_score\n",
"r2_score(ytest, yfit)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-15T07:07:37.931538Z",
"start_time": "2018-05-15T07:07:37.811851Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x, y)\n",
"plt.plot(xfit, yfit);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Supervised learning example: Iris classification\n",
"\n",
"> **Question**: given a model trained on a portion of the Iris data, how well can we predict the remaining labels?\n",
"\n",
"For this task, we will use an extremely simple generative model known as **Gaussian naive Bayes** \n",
"- which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution\n",
"- see In Depth: Naive Bayes Classificationfor more details).\n",
"- it is so fast \n",
"- it has no hyperparameters to choose\n",
"\n",
"Gaussian naive Bayes is often a good model to use as a baseline classification, before exploring whether improvements can be found through more sophisticated models."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"To evaluate the model on data it has not seen before\n",
"\n",
"- we will split the data into a *training set* and a *testing set*.\n",
" - Using the ``train_test_split`` utility function:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:39:20.821771Z",
"start_time": "2021-05-21T02:39:20.817440Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,\n",
" random_state=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"With the data arranged, we can follow our recipe to predict the labels:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:40:35.538363Z",
"start_time": "2021-05-21T02:40:35.527985Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from sklearn.naive_bayes import GaussianNB # 1. choose model class\n",
"model = GaussianNB() # 2. instantiate model\n",
"model.fit(Xtrain, ytrain) # 3. fit model to data\n",
"y_model = model.predict(Xtest) # 4. predict on new data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:40:50.182153Z",
"start_time": "2021-05-21T02:40:50.174815Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('setosa', 'setosa') ('versicolor', 'versicolor') ('versicolor', 'versicolor') ('setosa', 'setosa') ('virginica', 'virginica') ('versicolor', 'versicolor') ('virginica', 'virginica') ('setosa', 'setosa') ('setosa', 'setosa') ('virginica', 'virginica') ('versicolor', 'versicolor') ('setosa', 'setosa') ('virginica', 'virginica') ('versicolor', 'versicolor') ('versicolor', 'versicolor') ('setosa', 'setosa') ('versicolor', 'versicolor') ('versicolor', 'versicolor') ('setosa', 'setosa') ('setosa', 'setosa') ('versicolor', 'versicolor') ('versicolor', 'versicolor') ('versicolor', 'virginica') ('setosa', 'setosa') ('virginica', 'virginica') ('versicolor', 'versicolor') ('setosa', 'setosa') ('setosa', 'setosa') ('versicolor', 'versicolor') ('virginica', 'virginica') ('versicolor', 'versicolor') ('virginica', 'virginica') ('versicolor', 'versicolor') ('virginica', 'virginica') ('virginica', 'virginica') ('setosa', 'setosa') ('versicolor', 'versicolor') ('setosa', 'setosa')\n"
]
}
],
"source": [
"print(*zip(ytest, y_model))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:41:40.447104Z",
"start_time": "2021-05-21T02:41:40.443174Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.9736842105263158"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(ytest, y_model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Unsupervised learning example: Iris dimensionality reduction\n",
"\n",
"Reducing the dimensionality of the Iris data to more easily visualize it:\n",
"- Iris data is four dimensional: \n",
" - there are four features recorded for each sample.\n",
"\n",
"The task of dimensionality reduction is to ask:\n",
"\n",
"> whether there is a suitable lower-dimensional representation that retains the essential features of the data."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Unsupervised learning example: Iris dimensionality reduction\n",
"\n",
"Dimensionality reduction is often used as an aid to visualizing data: \n",
"- it is much easier to plot data in two dimensions than in four dimensions or higher!\n",
"\n",
"Here we will use ``principal component analysis`` (PCA)\n",
"- It is a fast linear dimensionality reduction technique.\n",
"\n",
"We will ask the model to return \n",
"- two components\n",
" - a two-dimensional representation of the data.\n",
"\n",
"Following the sequence of steps outlined earlier:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:48:52.435037Z",
"start_time": "2021-05-21T02:48:52.378386Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA # 1. Choose the model class\n",
"model = PCA(n_components=2) # 2. Instantiate the model with hyperparameters\n",
"model.fit(X_iris) # 3. Fit to data. Notice y is not specified!\n",
"X_2D = model.transform(X_iris) # 4. Transform the data to two dimensions"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"To plot the results:\n",
"- A quick way to do this is to insert the results into the original Iris ``DataFrame``, \n",
"- use Seaborn's ``lmplot`` to show the results:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:49:27.030967Z",
"start_time": "2021-05-21T02:49:26.785531Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_context(\"talk\", font_scale=1.5)\n",
"iris['PCA1'] = X_2D[:, 0]\n",
"iris['PCA2'] = X_2D[:, 1]\n",
"sns.lmplot(x = \"PCA1\", y= \"PCA2\", hue='species', data=iris, fit_reg=False);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"In the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n",
"\n",
"A relatively straightforward classification will probably be effective on the dataset."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Unsupervised learning: Iris clustering\n",
"\n",
"Let's next look at applying clustering to the Iris data.\n",
"\n",
"> A clustering algorithm attempts to find distinct groups of data without reference to any labels.\n",
"\n",
"We will use a powerful clustering method called a ``Gaussian mixture model (GMM)``\n",
"- more detail in **In Depth: Gaussian Mixture Models**.\n",
"- A GMM attempts to model the data as a collection of Gaussian blobs.\n",
"\n",
"We can fit the Gaussian mixture model as follows:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:54:14.242889Z",
"start_time": "2021-05-21T02:54:14.225979Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from sklearn.mixture import GaussianMixture # 1. Choose the model class\n",
"model = GaussianMixture(n_components=3,\n",
" covariance_type='full') # 2. Instantiate the model with hyperparameters\n",
"model.fit(X_iris) # 3. Fit to data. Notice y is not specified!\n",
"y_gmm = model.predict(X_iris) # 4. Determine cluster labels"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"As before, we will \n",
"- add the cluster label to the Iris ``DataFrame`` and \n",
"- use Seaborn to plot the results:"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-15T10:05:36.294395Z",
"start_time": "2018-05-15T10:05:36.289669Z"
},
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'axes.labelsize': 11.0,\n",
" 'axes.titlesize': 12.0,\n",
" 'font.size': 50.0,\n",
" 'grid.linewidth': 1.0,\n",
" 'legend.fontsize': 10.0,\n",
" 'lines.linewidth': 1.75,\n",
" 'lines.markeredgewidth': 0.0,\n",
" 'lines.markersize': 7.0,\n",
" 'patch.linewidth': 0.3,\n",
" 'xtick.labelsize': 10.0,\n",
" 'xtick.major.pad': 7.0,\n",
" 'xtick.major.width': 1.0,\n",
" 'xtick.minor.width': 0.5,\n",
" 'ytick.labelsize': 10.0,\n",
" 'ytick.major.pad': 7.0,\n",
" 'ytick.major.width': 1.0,\n",
" 'ytick.minor.width': 0.5}"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sns.plotting_context()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:54:27.138818Z",
"start_time": "2021-05-21T02:54:26.528216Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_context(\"talk\", font_scale=1.5)\n",
"iris['cluster'] = y_gmm\n",
"sns.lmplot(x =\"PCA1\", y = \"PCA2\", data=iris, hue='species',\n",
" col='cluster', fit_reg=False);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"By splitting the data by cluster number, GMM algorithm recovered the underlying label without an expert: \n",
"- the measurements of these flowers are distinct enough\n",
"- we could *automatically* identify the presence of these different groups of species \n",
" - with a simple clustering algorithm!\n",
"- might further give experts in the field clues as to the relationship between the samples they are observing."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Application: Exploring Hand-written Digits"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"In the wild, this problem involves \n",
"- locating characters in an image. \n",
"- identifying characters in an image. \n",
"\n",
"Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Loading and visualizing the digits data\n",
"\n",
"We'll use Scikit-Learn's data access interface and take a look at this data:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T02:57:54.436841Z",
"start_time": "2021-05-21T02:57:54.251535Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1797, 8, 8)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.datasets import load_digits\n",
"digits = load_digits()\n",
"digits.images.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"The images data is a three-dimensional array: \n",
"- 1,797 samples \n",
"- each consisting of an 8 × 8 grid of pixels.\n",
"\n",
"Let's visualize the first hundred of these:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T03:02:25.561610Z",
"start_time": "2021-05-21T03:02:23.002802Z"
},
"code_folding": [
2
],
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt \n",
"fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
" subplot_kw={'xticks':[], 'yticks':[]},\n",
" gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
"for i, ax in enumerate(axes.flat):\n",
" ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n",
" ax.text(0.05, 0.05, str(digits.target[i]),\n",
" transform=ax.transAxes, color='green')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"In order to work with this data within Scikit-Learn, \n",
"- we need a two-dimensional, ``[n_samples, n_features]`` representation.\n",
"- treating each pixel in the image as a feature: \n",
" - so that we have a length-64 array of pixel values representing each digit.\n",
"- target array gives the previously determined label for each digit.\n",
"\n",
"Features and targets are represented as the ``data`` and ``target`` attributes in the `digits` dataset respectively:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T03:04:05.162242Z",
"start_time": "2021-05-21T03:04:05.158481Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1797, 64)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = digits.data\n",
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-15T13:02:53.880019Z",
"start_time": "2018-05-15T13:02:53.875168Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 5., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 10., 0., 0.],\n",
" [ 0., 0., 0., ..., 16., 9., 0.],\n",
" ...,\n",
" [ 0., 0., 1., ..., 6., 0., 0.],\n",
" [ 0., 0., 2., ..., 12., 0., 0.],\n",
" [ 0., 0., 10., ..., 12., 1., 0.]])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T03:04:14.697779Z",
"start_time": "2021-05-21T03:04:14.693925Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1797,)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = digits.target\n",
"y.shape "
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-15T13:03:31.333742Z",
"start_time": "2018-05-15T13:03:31.329466Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, ..., 8, 9, 8])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We see here that there are 1,797 samples and 64 features."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Unsupervised learning: Dimensionality reduction\n",
"\n",
"We'd like to visualize our points within the 64-dimensional parameter space\n",
"- it's difficult to effectively visualize points in such a high-dimensional space.\n",
"- Instead we'll reduce the dimensions to 2, using an unsupervised method.\n",
"\n",
"Here, we'll make use of a manifold learning algorithm called *Isomap* (see **In-Depth: Manifold Learning**), and transform the data to two dimensions:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T03:04:39.507816Z",
"start_time": "2021-05-21T03:04:37.757176Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1797, 2)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.manifold import Isomap\n",
"iso = Isomap(n_components=2)\n",
"iso.fit(digits.data)\n",
"data_projected = iso.transform(digits.data)\n",
"data_projected.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We see that the projected data is now two-dimensional.\n",
"Let's plot this data to see if we can learn anything from its structure:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T03:04:48.775740Z",
"start_time": "2021-05-21T03:04:48.571029Z"
},
"code_folding": [
0
],
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\n",
" edgecolor='none', alpha=0.5,\n",
" cmap=plt.cm.get_cmap('nipy_spectral', 10))\n",
"plt.colorbar(label='digit label', ticks=range(10))\n",
"plt.clim(-0.5, 9.5);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. \n",
"\n",
"- zeros (in black) and ones (in purple) have very little overlap in parameter space.\n",
" - Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n",
"- There seems to be a more or less continuous spectrum between ones and fours: \n",
" - we can understand this by realizing that some people draw ones with \"hats\" on them, which cause them to look similar to fours.\n",
"\n",
"Overall, however, the different groups appear to be fairly well separated in the parameter space: \n",
"- this tells us that even a very straightforward supervised classification algorithm should perform suitably on this data.\n",
"\n",
"Let's give it a try."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Classification on digits\n",
"\n",
"Let's apply a classification algorithm to the digits.\n",
"\n",
"- split the data into a training and testing set\n",
"- fit a Gaussian naive Bayes model"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T03:06:26.941819Z",
"start_time": "2021-05-21T03:06:26.938082Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T03:06:47.121578Z",
"start_time": "2021-05-21T03:06:47.113307Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from sklearn.naive_bayes import GaussianNB\n",
"model = GaussianNB()\n",
"model.fit(Xtrain, ytrain)\n",
"y_model = model.predict(Xtest)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T03:08:47.202493Z",
"start_time": "2021-05-21T03:08:47.197777Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.8333333333333334"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(ytest, y_model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"With even this extremely simple model, we find about 80% accuracy for classification of the digits!\n",
"\n",
"However, this single number doesn't tell us *where* we've gone wrong\n",
"- one nice way to do this is to use the *confusion matrix*, \n",
" - which we can compute with Scikit-Learn and plot with Seaborn:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"ExecuteTime": {
"end_time": "2021-05-21T03:10:02.065966Z",
"start_time": "2021-05-21T03:10:01.690660Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"sns.set_context(\"notebook\", font_scale=1.7)\n",
"\n",
"mat = confusion_matrix(ytest, y_model)\n",
"\n",
"sns.heatmap(mat, square=True, annot=True, cbar=False)\n",
"plt.xlabel('predicted value')\n",
"plt.ylabel('true value');"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"This shows us where the mis-labeled points tend to be: \n",
"- a large number of twos here are mis-classified as either ones or eights.\n",
"\n",
"Another way to gain intuition into the characteristics of the model:\n",
"- to plot the inputs again, with their predicted labels.\n",
"- using green for correct labels, and red for incorrect labels:"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-15T13:17:10.256934Z",
"start_time": "2018-05-15T13:17:07.529831Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
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"source": [
"fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
" subplot_kw={'xticks':[], 'yticks':[]},\n",
" gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
"\n",
"test_images = Xtest.reshape(-1, 8, 8)\n",
"\n",
"for i, ax in enumerate(axes.flat):\n",
" ax.imshow(test_images[i], cmap='binary', interpolation='nearest')\n",
" ax.text(0.05, 0.05, str(y_model[i]),\n",
" transform=ax.transAxes,\n",
" color='green' if (ytest[i] == y_model[i]) else 'red')"
]
},
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"Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally.\n",
"\n",
"To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as \n",
"- support vector machines (see **In-Depth: Support Vector Machines**), \n",
"- random forests (see **In-Depth: Decision Trees and Random Forests**) \n",
"- the other classification approaches."
]
},
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"source": [
"## Summary"
]
},
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"In this section we have covered the essential features of the Scikit-Learn \n",
"- data representation\n",
"- the estimator API.\n",
"\n",
"Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds.\n",
"\n",
"Armed with this information about the estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data.\n",
"\n",
"In the next section, we will explore perhaps the most important topic in machine learning: how to select and validate your model."
]
}
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