intro to machine learning with scikit learn

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Yossi Cohen

Machine Learning with Scikit-learn

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INTRO TO ML PROGRAMMING

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ML Programming

1. Get Data Get labels for supervised learning

2. Create a classifier

3. Train the classifier

4. Predict test data

5. Evaluate predictor accuracy

*Configure and improve by repeating 2-5

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The ML Process

Filter

OutliersRegression

Classify

Validateconfigure

Model

Partition

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Get Data & Labels• Sources

–Open data sources–Collect on your own

• Verify data validity and correctness• Wrangle data

–make it readable by computer–Filter it

• Remove Outliers

PANDAS Python library could assist in pre-processing & data manipulation before ML http://pandas.pydata.org/

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Pre-Processing

Change formattingRemove redundant data Filter Data (take partial data)Remove OutliersLabelSplit for testing (10/90, 20/80)

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Data Partitioning

• Data and labels–{[data], [labels]} –{[3,7, 76, 11, 22, 37, 56,2],[T, T, F, T, F, F, F, T]}–Data: [Age, Do you love Nutella?]

• Partitioning will create–{[train data], [train labels],[test data], [test labels]}–We usually split the data on a ration of 9:1–There is a tradeoff between the effectiveness of the test and the learning we could provide to the classifier

• We will look at a partitioning function later

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Learn (The “Smart Part”)

ClassificationIf the output is discrete to a limited amount of classes (groups)

RegressionIf the output is continues

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Learn Programming

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Create Classifier

For most SUPERVISED LEARNING algorithms this would be

C = ClassifyAlg(Params)Its up to us (ML guys) to set the best

paramsHow?

1. We could develop a hunch for it2. Perform an exhaustive search

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Train the classifier

We assigned

C = ClassifyAlg(Params)

This is a general algorithm with some initalizer and configurations.

In this stage we train it using:

C.fit(Data, Labels)

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Predict

After we have a trained Algorithm classifier C

Prdeicted_Labels = C.predict(Data)

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Predictor Evaluation

We are not done yetThere is a need to evaluate the predictor

accuracy in comparison to other predictors and to the system requirements

We will learn several methods for this

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ENVIRONMENT

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The Environment

• There are many existing environments and tools we could use–Matlab with Machine learning toolbox–Apache Mahout –Python with Scikit-learn

• Additional tools–Hadoop / Map-Reduce to accelerate and parallelize large data set processing

–Amazon ML tools–NVIDIA Tools

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Scikit-learn

• Installation Instructions inhttp://scikit-learn.org/stable/install.html#install-official-release

• Depends on two other libraries• numpy and scipy

• Easiest way to install on windows:• Install WinPython

http://sourceforge.net/projects/winpython/files/WinPython_2.7/2.7.9.4/

–Lets install this togetherFor Linux / Mac computers just install the 3

libs separately using PIP

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THE DATA

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Data sets

There are many data sets to work onOne of them is the Iris data classification

into three groups. It has an interesting story you could google later

Well work on the iris data

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Lab A – Plot the Iris data

Plot septal length vs septal width with labels ONLYHow? Google Iris data and the scikit learn environment

Try to understand the second part of the program with the PCA

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Iris Dataimport matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

from sklearn import datasets

iris = datasets.load_iris()

X = iris.data[:, :2] # we only take the first two features.

Y = iris.target

x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5

y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5

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Plot Iris Data

plt.figure(2, figsize=(8, 6))

plt.clf()

plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)

plt.xlabel('Sepal length')

plt.ylabel('Sepal width')

plt.xlim(x_min, x_max)

plt.ylim(y_min, y_max)

plt.xticks(())

plt.yticks(())

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Add PCA for better classificationfig = plt.figure(1, figsize=(8, 6))

ax = Axes3D(fig, elev=-150, azim=110)

X_reduced = PCA(n_components=3).fit_transform(iris.data)

ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,

cmap=plt.cm.Paired)

ax.set_title("First three PCA directions")

ax.set_xlabel("1st eigenvector")

ax.w_xaxis.set_ticklabels([])

ax.set_ylabel("2nd eigenvector")

ax.w_yaxis.set_ticklabels([])

ax.set_zlabel("3rd eigenvector")

ax.w_zaxis.set_ticklabels([])

plt.show()

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Iris Data Classified

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Thank you!More About me:

Yossi CohenYossi Cohenyossicohen19@gmail.com+972-545-313092+972-545-313092

Video compression and computer vision enthusiast & lecturer

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