statistical methods for big data in life sciences and ...€¦ · statistical methods for big data...
TRANSCRIPT
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Statistical methods for big data
in life sciences and health with R
Philippe Jacquet
Data Scientist at CADMOS
June 2018
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CADMOS
CADMOS: Center for Advanced Modeling Of Science
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CADMOS
CADMOS: Center for Advanced Modeling Of Science
Free service for all UNIL, EPFL and UNIGE researchers
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CADMOS
CADMOS: Center for Advanced Modeling Of Science
Free service for all UNIL, EPFL and UNIGE researchers
Help with HPC parallel computing (code optimization) orBig Data analysis (pipeline development), including somecomputing power at SCITAS (the EPFL cluster)
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CADMOS
CADMOS: Center for Advanced Modeling Of Science
Free service for all UNIL, EPFL and UNIGE researchers
Help with HPC parallel computing (code optimization) orBig Data analysis (pipeline development), including somecomputing power at SCITAS (the EPFL cluster)
Contact:UNIL: [email protected], [email protected]: [email protected]: [email protected]
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Outline
TODAY
13:30 – 15:00 Lecture on Neural Network
15:00 – 15:30 Coffee break
15:30 – 17:00 Practicals on Neural Network
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Outline
TODAY
13:30 – 15:00 Lecture on Neural Network
15:00 – 15:30 Coffee break
15:30 – 17:00 Practicals on Neural Network
TOMORROW (Room 321 - Amphipôle)
09:00 – 10:30 Lecture on Decision Tree and Random Forest
10:30 – 11:00 Coffee break
11:00 – 12:30 Practicals on Decision Tree and Random Forest
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Outline
If you have any question during the lecture
you are welcome to ask
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Outline
We will analyse the same dataset (iris data)
with all the methods
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Outline
And present some applications
in health science
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The iris dataset
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The iris dataset
4 measurements on 50 flowers in 3 species
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The iris dataset
Sepal length Sepal width Petal length Petal width Species
5.1 3.5 1.4 0.2 setosa
7.0 3.2 4.7 1.4 versicolor
6.3 3.3 6.0 2.5 virginica
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The iris dataset
Sepal length Sepal width Petal length Petal width Species
5.1 3.5 1.4 0.2 setosa
7.0 3.2 4.7 1.4 versicolor
6.3 3.3 6.0 2.5 virginica
input output
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The iris dataset
GOAL
Given an input (4 measurements)
predict its output (species)
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Machine learning
MACHINE LEARNING
UNSUPERVISED LEARNING- only input data- explorative models: clustering, dim. reduction
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Machine learning
MACHINE LEARNING
UNSUPERVISED LEARNING- only input data- explorative models: clustering, dim. reduction
SUPERVISED LEARNING- input data + output data- predictive models: classi�cation, regression
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Supervised learning
Data splitting:
train validation test
0 60% 80% 100%
model selection model assessementmodel training
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Supervised learning
Data splitting:
train validation test
0 60% 80% 100%
model selection model assessementmodel training
Model training: estimate the model parameters
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Supervised learning
Data splitting:
train validation test
0 60% 80% 100%
model selection model assessementmodel training
Model training: estimate the model parameters
Model selection: estimate the performance of differentmodels in order to choose the best one
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Supervised learning
Data splitting:
train validation test
0 60% 80% 100%
model selection model assessementmodel training
Model training: estimate the model parameters
Model selection: estimate the performance of differentmodels in order to choose the best one
Model assessment: having chosen a final model, estimateits prediction accuracy on new data
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Supervised learning
EXAMPLE
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Supervised learning
Model 1= Neural Network
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Supervised learning
Model 1= Neural Network
Estimate the model parameters using the training set
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Supervised learning
Model 1= Neural Network
Estimate the model parameters using the training set
Measure the model accuracy based on the validation set
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Supervised learning
Model 2 = Decision Tree
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Supervised learning
Model 2 = Decision Tree
Estimate the model parameters using the training set
Measure the model accuracy based on the validation set
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Supervised learning
Model 3 = Random Forest
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Supervised learning
Model 3 = Random Forest
Estimate the model parameters using the training set
Measure the model accuracy based on the validation set
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Supervised learning
Model Selection
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Supervised learning
Model Selection
Select the best model based on its accuracy
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Supervised learning
Model Selection
Select the best model based on its accuracy
Neural Network = Decision Tree
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Supervised learning
Model Assessment
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Supervised learning
Model Assessment
Mesure best model accuracy on the test set
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Supervised learning
Model Assessment
Mesure best model accuracy on the test set
THE END
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Neural network for the iris data
How to build a neural network
for the iris data ?
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
y1
y2
y3
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerMany Hidden Layers
y1
y2
y3
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Deep Learning = More than 1 Hidden Layer
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
y1
y2
y3
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights Weights
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
![Page 42: Statistical methods for big data in life sciences and ...€¦ · Statistical methods for big data in life sciences and health with R Philippe Jacquet Data Scientist at CADMOS June](https://reader034.vdocument.in/reader034/viewer/2022051806/5ffee68eda9cfe0eac053a4b/html5/thumbnails/42.jpg)
Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
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Neural network for the iris data
What is
the mathematical formula
for this neural network ?
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
X W(1) Z(1) ReLU Z(2) W(2) Z(3) SoftMAX Y
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
W(1) Z(1) ReLU Z(2) W(2) Z(3) SoftMAX Y
X
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
Z(1) ReLU Z(2) W(2) Z(3) SoftMAX Y
W (1)X
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
ReLU Z(2) W(2) Z(3) SoftMAX Y
Z (1)
� �� �W (1)X
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
Z(2) W(2) Z(3) SoftMAX Y
ReLU(
Z (1)
� �� �W (1)X )
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
W(2) Z(3) SoftMAX Y
ReLU(
Z (1)
� �� �W (1)X )� �� �
Z (2)
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
Z(3) SoftMAX Y
W (2) ReLU(
Z (1)
� �� �W (1)X )� �� �
Z (2)
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
Y = SoftMax(W (2) ReLU(
Z (1)
� �� �W (1)X )� �� �
Z (2)
)
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Neural network for the iris data
In summary
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Neural network for the iris data
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
Y = f (X ) = SoftMax(W (2)ReLU(W (1)X ))
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Forward propagation
In forward propagation, we go from X to Y by applying a seriesof functional transformations :
Y = SoftMax(W (2)ReLU(W (1)X ))
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Forward propagation
In forward propagation, we go from X to Y by applying a seriesof functional transformations :
Y = SoftMax(W (2)ReLU(W (1)X ))
Questions:
How do we determine the weights W (1) and W (2) ?
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Forward propagation
In forward propagation, we go from X to Y by applying a seriesof functional transformations :
Y = SoftMax(W (2)ReLU(W (1)X ))
Questions:
How do we determine the weights W (1) and W (2) ?
Why do we need the activation function ReLU ?
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Forward propagation
In forward propagation, we go from X to Y by applying a seriesof functional transformations :
Y = SoftMax(W (2)ReLU(W (1)X ))
Questions:
How do we determine the weights W (1) and W (2) ?
Why do we need the activation function ReLU ?
Why do we use the SoftMax function at the end ?
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Weight matrices
How do we determine
the weight values ?
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Weight matrices
We want to choose the weights to make
the best predictions possible
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Weight matrices
We will use an algorithm
to find these weights
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Weight matrices
This algorithm needs
some initial weight values
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Weight matrices
How do we choose
the initial weight values ?
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Weight matrices
The naive choice
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Weight matrices
We set all the weights to zero
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Weight matrices
All the predictions are the same:
P=1/3 for the three species
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Weight matrices
The weights are updated by the same amount
and thus cannot get unequal values
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Weight matrices
The network cannot learn
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Weight matrices
A good choice
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Weight matrices
Random initialisation around 0
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Weight matrices
We choose the weights according to
a normal distribution N (0, σ2)
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Weight matrices
We choose the weights according to
a normal distribution N (0, σ2)
The value of σ2 affects the performance
(speed and accuracy)
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Weight matrices
Two common choices
σ2 =1
Nin(Xavier)
σ2 =2
Nin + Nout(Glorot & Bengio)
Nin = the number of nodes going in the weight matrix W
Nout = the number of nodes going out the weight matrix W
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Weight matrices
Probabilistic framework
Linear model:
Y = W X
Yi =
Nin�
j=1
Wij Xj , i = 1, . . . ,Nout
Assumptions: Xj ∼ N (0,σ2x ) and Wij ∼ N (0,σ2)
The condition V (Yi) = V (Xj) leads to the values for σ2:Xavier and Glorot (when including backpropagation)
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Weight matrices
EXAMPLE
x1
x2
x3
x4
Input Layer Hidden Layer
z(1)2
z(1)3
z(1)4
z(1)5
z(1)1
w(1)11
w(1)54
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Weight matrices
EXAMPLE
x1
x2
x3
x4
Input Layer Hidden Layer
z(1)2
z(1)3
z(1)4
z(1)5
z(1)1
w(1)11
w(1)54
Nin = 4
Nout = 5
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Weight matrices
EXAMPLE
x1
x2
x3
x4
Input Layer Hidden Layer
z(1)2
z(1)3
z(1)4
z(1)5
z(1)1
w(1)11
w(1)54
Nin = 4
Nout = 5
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STEP 1
Y = SoftMax(W (2)ReLU(W (1)X ))
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STEP 1
Y = SoftMax(W (2)ReLU(W (1)X ))
Sepal length Sepal width Petal length Petal width Species
5.1 3.5 1.4 0.2 setosa
7.0 3.2 4.7 1.4 versicolor
6.3 3.3 6.0 2.5 virginica
input output
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STEP 1
Y = SoftMax(W (2)ReLU(W (1)X ))
Matrix multiplication (σ = 0.5):
W (1) X =
0.5 0.1 −0.2 −0.4−0.4 1.0 0.5 1.0−0.2 −0.2 −0.5 −0.10.2 0.7 0.3 0.20.6 0.6 0.1 −0.4
5.13.51.40.2
=
5.42.22.9−1.7−2.4
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Activation function
Why do we need
the activation function ReLU ?
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Activation function
A neural network without any non-linear activation functionwould simply be a linear regression model
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Activation function
A neural network without any non-linear activation functionwould simply be a linear regression model
There are several possible non-linear activation functions:
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Activation function
A neural network without any non-linear activation functionwould simply be a linear regression model
There are several possible non-linear activation functions:
ReLU is very simple and very popular in deep neural networks
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STEP 2
Y = SoftMax(W (2)ReLU(W (1)X ))
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STEP 2
Y = SoftMax(W (2)ReLU(W (1)X ))
Activation function (ReLU):
ReLU(W (1) X ) = ReLU(
5.42.22.9−1.7−2.4
) =
5.42.22.900
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STEP 3
Y = SoftMax(W (2)ReLU(W (1)X ))
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STEP 3
Y = SoftMax(W (2)ReLU(W (1)X ))
Matrix multiplication (σ = 0.5):
W (2)ReLU(W (1)X ) =
0.6 0.1 0.9 −0.2 −0.50.3 −0.3 0.3 −0.9 −0.90.3 0.2 0.4 −1.0 0.6
5.42.22.900
=
1.90.32.9
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SoftMax function
Why do we use the
SoftMax function ?
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SoftMax function
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
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SoftMax function
x1
x2
x3
x4
Input Layer Output LayerHidden Layer
z(1) y1
y2
y3
2
z(1)3
z(1)4
z(1)5
z(1)1
z(2)2
z(2)3
z(2)4
z(2)5
z(2)1
z(3)2
z(3)3
z(3)1
Soft
Max
w(1)11
w(1)54
w(2)11
w(2)35
Sepal LengthProb. Setosa
Sepal Width
Petal Length
Petal Width
Prob. Versicolor
Prob. Virginica
Weights WeightsActivationFunction
The output Z (3) are arbitrary real values and we want to convertthem into probabilities:
SoftMax(Z (3))i =ez(3)
i
ez(3)1 + ez(3)
2 + ez(3)3
= Prob. Class i ∈ (0, 1)
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SoftMax function
Binary classification
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SoftMax function
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SoftMax function
Binary classification:
Empirical observation - logistic regression:
Motivate - sigmoid function (1 output neuron):
wikipedia
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SoftMax function
Binary classification:
Empirical observation - logistic regression:
Motivate - sigmoid function (1 output neuron):
wikipedia
Multinomial logistic regression: SoftMax function (K classes):
P(X )� =eβ�X
�Kk=1 eβk X
SoftMax(X )� =eX�
�Kk=1 eXk
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STEP 4
Y = SoftMax(W (2)ReLU(W (1)X ))
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STEP 4
Y = SoftMax(W (2)ReLU(W (1)X ))
SoftMax function:
SoftMax(W (2)ReLU(W (1)X )) = SoftMax(
1.90.32.9
) =
0.250.050.70
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STEP 4
Y = SoftMax(W (2)ReLU(W (1)X ))
SoftMax function:
SoftMax(W (2)ReLU(W (1)X )) = SoftMax(
1.90.32.9
) =
0.250.050.70
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STEP 4
Y = SoftMax(W (2)ReLU(W (1)X ))
SoftMax function:
SoftMax(W (2)ReLU(W (1)X )) = SoftMax(
1.90.32.9
) =
0.250.050.70
Prediction: Virginica
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STEP 4
Y = SoftMax(W (2)ReLU(W (1)X ))
SoftMax function:
SoftMax(W (2)ReLU(W (1)X )) = SoftMax(
1.90.32.9
) =
0.250.050.70
Prediction: Virginica This is wrong! It is Setosa
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Network training using gradient descent
What to do when
the prediction is wrong ?