# Machine Learning

## Machine Learning

We'll be returning to material in the Python Data Science Handbook:

Also notable: Andrew Ng's Coursera course Machine Learning.

## Machine Learning

Compared to CMPT 419/726:

I'm less concerned about machine learning as a topic of study, and more concerned about using it as a tool to make sense of data.

Obviously, 2–3 weeks will cover a lot less detail than a whole course.

We will generally use the scikit-learn libraries which implements common ML algorithms for Python.

## What is ML?

Usual parts we'll have (for supervised ML):

• A task: something we want to predict: calculate/​estimate/​discover/​etc.
• Data/​experience: sample inputs and corresponding correct outputs.
• A measure of success: if we make different predictions, how do we decide which one is better?

## What is ML?

We want to take the sample inputs and build a model that captures the calculation that is needed to produce correct output.

If we have done it right, the model should be able to predict output for new inputs that we haven't seen before.

## What is ML?

These models of the problem won't be completely designed by us.

We will create the overall design of the model, and train it on the known inputs/​outputs.

## Linear Regression

We just talked about linear regression as a statistical technique. It turns out that the machine learning people claim it too…

## Linear Regression

What we did before:

from scipy import stats
reg = stats.linregress(x, y)
print(reg.slope, reg.intercept)
0.523895506988829 -1.503281343279347

## Linear Regression

The same basic operation with scikit-learn to get the same results:

from sklearn.linear_model import LinearRegression
X = np.stack([x], axis=1)
model = LinearRegression(fit_intercept=True)
model.fit(X, y)
print(model.coef_[0], model.intercept_)
0.5238955069888295 -1.5032813432793506

## Linear Regression

Things to note…

The np.stack function is used to join 1D arrays (in this case, only one) into a 2D array.

arr = np.array([1,2,3])
print(arr)
print(np.stack([arr], axis=1))
[1 2 3]
[[1]
[2]
[3]]

In this example, .reshape(-1, 1) would also work.

If you already have 2D arrays, np.concatenate will put them together into a single 2D array.

## Linear Regression

The ML convention is to have 2D arrays (matrices) in capitalized variable names (X vs x), which isn't standard Python style.

X = np.stack([x], axis=1)

I hate it, but I'll go with it.

## Linear Regression

Another convention: scikit-learn uses trailing underscores to indicate values that were estimated/​fitted/​learned. *

print(model.coef_[0], model.intercept_)

Also not very Pythonic, but there it is.

## Linear Regression

Style aside, the operations we're doing are:

model = LinearRegression(fit_intercept=True)
model.fit(X, y)
1. Create a linear model. i.e. we will understand (model) the input → output predictions as a linear relationship.
2. Use the known input and output values to fit the model to the data.

## Linear Regression

Fit: compute the parameters that do the best job matching the model to the given data.

Also called training the model. The known correct inputs/​outputs are the training data.

For this model, the parameters are slope and intercept. Best is determined by the smallest sum of square error.

## Linear Regression

Once we have a model, we can use it to make predictions.

X_fit = [[15], [-19]]
y_fit = model.predict(X_fit)
print(y_fit)
[  6.35515126 -11.45729598]

i.e. when the input is [15], the model predicts that the correct output is 6.35515126.

## Linear Regression

Or we can predict the entire range and draw it:

plt.plot(x, y, 'b.')
plt.plot(X_fit, y_fit, 'g.', markersize=25)
plt.plot(x, model.predict(X), 'r-')
plt.legend(['training data', 'specific predictions from prev slide', 'predicted line'])

## Linear Regression

Summary:

• Model: the way we're understanding the problem, and making predictions.
• Parameters: values in the model that determine how it predicts.
• Fit/​Train: using training data to get good values for the parameters.
• Predict: using the model to guess the correct output for new input.

## The Intercept

When setting up the linear fit, we could have done the intercept manually like this:

X_with = np.concatenate([np.ones(X.shape), X], axis=1)
print(X_with[:4])
[[ 1.         29.16862815]
[ 1.         12.8018865 ]
[ 1.         19.74485581]
[ 1.         34.89071839]]

Then we don't need the fit to worry about an intercept…

## The Intercept

Then we don't need the regression to worry about an intercept.

model = LinearRegression(fit_intercept=False)
model.fit(X_with, y)
print(model.coef_)
[-1.50328134  0.52389551]

Same intercept and slope. We basically just fit two $$a_i$$ parameters in:

$y = a_0 1 + a_1 x\,.$

## Polynomial Regression

What do we do when we get non-linear values, where a line won't be a good model?

## Polynomial Regression

Obvious answer: fit something with more freedom than just a line. We're going to need a different function with more parameters.

## Polynomial Regression

If we can create an array with $$1=x^0$$ and $$x=x^1$$ values for a linear fit, we can do more of the same.

And, we don't even have to do it manually.

from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=3, include_bias=True)
X = np.array([[2], [3], [4]])
print(poly.fit_transform(X))
[[ 1.  2.  4.  8.]
[ 1.  3.  9. 27.]
[ 1.  4. 16. 64.]]

## Polynomial Regression

[[ 1.  2.  4.  8.]
[ 1.  3.  9. 27.]
[ 1.  4. 16. 64.]]

If we can do a linear fit to that input, we're finding $$a_i$$ parameters to fit this to the data:

$y = a_0 x^0 + a_1 x^1 + a_2 x^2 + a_3 x^3\,.$

## Polynomial Regression

We can use this to predict with a polynomial:

poly = PolynomialFeatures(degree=3, include_bias=True)
X_poly = poly.fit_transform(X)
model = LinearRegression(fit_intercept=False)
model.fit(X_poly, y)

## Polynomial Regression

What did the fit really decide?

print(model.coef_)
[ 5.16190050e-01  1.29319129e-01 -4.21668370e-04 -1.06965776e-03]

In other words, our predictions are,

$y_{\mathrm{pred}} = 0.516 + 0.129 x - 0.000422 x^2 - 0.00107 x^3\,.$

## Polynomial Regression

That seems useful: we can use higher-degree polynomial inputs and simple linear fitting to allow more flexibility.

A higher-degree polynomial allows the .fit() process more freedom to fit the data better.

## Polynomial Regression

If some is good, more must be better.

poly = PolynomialFeatures(degree=11, include_bias=True)
X_poly = poly.fit_transform(X)
model = LinearRegression(fit_intercept=False)
model.fit(X_poly, y)

## Polynomial Regression

The fit line does an excellent job fitting the points, but a bad job fitting what I think is going on.

## Polynomial Regression

What we did: take an $$x$$ value and transform it to several $$f_1(x), f_2(x), \ldots, f_k(x)$$ values, then do a linear fit to find $$a_i$$ that give the best prediction,

$y_{\mathrm{predicted}} = a_1 f_1(x) + a_2 f_2(x) + \cdots + a_k f_k(x)\,.$

The $$f_i$$ functions are basis functions.

## Polynomial Regression

There's nothing special about the powers (except that they're often useful).

These could have been any functions: Gaussian curves, sine/​cosine curves, logarithms/​exponentials, … . You can choose whatever makes sense for your data.

## ML Pipelines

It's common to have a situation like this: transform the data (in one, two, or more ways). Then fit some model to the result.

That becomes cumbersome: constantly must transform the X values when trying to use the model.

poly = PolynomialFeatures(degree=3, include_bias=True)
X_poly = poly.fit_transform(X)
model = LinearRegression(fit_intercept=False)
model.fit(X_poly, y)

And later,

plt.plot(X_range[:, 0], model.predict(poly.transform(X_range)), 'r-')

## ML Pipelines

Scikit-learn can combine several steps into a pipeline model:

from sklearn.pipeline import make_pipeline
model = make_pipeline(
PolynomialFeatures(degree=3, include_bias=True),
LinearRegression(fit_intercept=False)
)

This model takes our X values, creates polynomial features, and does a linear fit on them.

## ML Pipelines

Then we can treat this like a single model and not explicitly worry about the steps.

model = make_pipeline(
PolynomialFeatures(degree=3, include_bias=True),
LinearRegression(fit_intercept=False)
)
model.fit(X, y)
plt.plot(X_range[:, 0], model.predict(X_range), 'r-')

## ML Pipelines

Pipeline steps are all scikit-learn estimator objects.

Steps $$1$$ to $$n-1$$ will be transformers that can .fit() and .transform() to modify the values (or .fit_transform() which combines the two).

The last step is an estimator which can .fit() and .predict().

## ML Pipelines

It's easy to end up with several steps, and nice to not have to deal with each one every time you work with X values.

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.impute import SimpleImputer
model = make_pipeline(
SimpleImputer(strategy='mean'), # impute missing values
MinMaxScaler(),                 # scale each feature to 0-1
PolynomialFeatures(degree=3, include_bias=True),
LinearRegression(fit_intercept=False)
)

Course rule: if it can be a pipeline, it should be.

## ML Pipelines

We can create a pipeline that uses a custom basis function for linear regression. Our transformation function takes the X array and returns a new one.

def sigmoid_basis(X):  # [[x]] -> [[1, x, 1/(1 + e^x)]]
one = np.ones(X.shape)
sigmoid = 1 / (1 + np.exp(X))
return np.concatenate((one, X, sigmoid), axis=1)

from sklearn.preprocessing import FunctionTransformer
model = make_pipeline(
FunctionTransformer(sigmoid_basis, validate=True),
LinearRegression(fit_intercept=False)
)
model.fit(X, y)
print(model.named_steps['linearregression'].coef_)

## ML Pipelines

Results: excellent (particularly since I knew the data source when designing the basis functions)

[ 1.01205597 -0.00198257 -1.03079678]

## Training and Validation

The high-degree polynomial had a lot of freedom to fit the points, but somehow followed them too closely. How can we decide that is the wrong thing (for a real data set)?

## Training and Validation

The problem: we're relying too much on this exact data set. We train on it, and then claim it fits the data perfectly. Of course it does.

We are overfitting the training data.

We need to look at the model on some other data where we can get a more honest evaluation.

## Training and Validation

Common solution: break the available data up.

The usual thing to do is to split into training data which is used to fit the model, and validation data which is used to evaluate how well that went.

If the model does well on the validation data (which it hasn't seen before), that's a good sign.

## Training and Validation

As usual, we can do that in one line:

from sklearn.model_selection import train_test_split
X_train, X_valid, y_train, y_valid = train_test_split(X, y)
print(X_train.shape, X_valid.shape)
print(y_train.shape, y_valid.shape)
(45, 1) (15, 1)
(45,) (15,)

## Training and Validation

We can use the test data to produce a score: some value where larger = better fit.

model.fit(X_train, y_train)
print(model.score(X_valid, y_valid))
0.9742339805871324

We have some confidence that the score is related to how well the model will predict for new never-before-seen values.

## Training and Validation

It also makes some sense to compare the scores on the training and validation data. It will ususally be a little lower on the validation data, but being much smaller is a sign of overfitting.

print(model.score(X_train, y_train))
print(model.score(X_valid, y_valid))
0.9837583175794484
0.9742339805871324

## Training and Validation

The .score() method is vaguely equivalent to:

y_predicted = model.predict(X_valid)
score = somehow_score_predictions(y_valid, y_predicted)

But was the value we found good? We could explore…

## Training and Validation

def score_polyfit(n):
model = make_pipeline(
PolynomialFeatures(degree=n, include_bias=True),
LinearRegression(fit_intercept=False)
)
model.fit(X_train, y_train)
print('n=%i: score=%.5g' % (n, model.score(X_valid, y_valid)))

score_polyfit(1)
score_polyfit(5)
score_polyfit(9)
score_polyfit(13)
score_polyfit(17)
n=1: score=0.8202
n=5: score=0.97423
n=9: score=0.98873
n=13: score=0.98741
n=17: score=0.42944

## Training and Validation

Apparently degree 9 is pretty good.

n=1: score=0.8202
n=5: score=0.97423
n=9: score=0.98873
n=13: score=0.98741
n=17: score=0.42944

But (as is often the case) there are several values that are all quite good: it's hard to pick exactly one correct value. Probably degree 5–13 would be pretty good for this data.

## Training and Validation

It can also be useful to look at the scores on both the training and validation data.

trainscore = model.score(X_train, y_train)
validscore = model.score(X_valid, y_valid)
n=1: trainscore=0.8479 validscore=0.8202
n=5: trainscore=0.98376 validscore=0.97423
n=9: trainscore=0.99286 validscore=0.98873
n=13: trainscore=0.99321 validscore=0.98741
n=17: trainscore=0.67952 validscore=0.42944

If there's a big drop training to validation, it's usually a sign of overfitting.

## Training and Validation

This is the prediction of the degree 9 model, with training (blue) and testing points (green).

## Training and Validation

In case you're interested, the degree 17 model: