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From Black Box to Black Magic, Pycon Ireland 2014
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FROM BLACK BOX TO BLACK MAGIC
Daniele Trainini Lovera Gloria
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Automotive Sensor Market Worth $35.78 Billion by 2022
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VIRTUAL SENSORS3
WHY MACHINE LEARNING?
20/50 ENGINE SIGNALS
Data gathering
Raw Data
TEST
Data storage
Data analysis
Features selection
Data preprocessing
Model Selection
Results analysis
Experiments
Params calibration
WORKFLOW
DB
Data analysis
Features selection
Data preprocessing
Fx and Fy as functions of the longitudinal slip “k” and side slip angle β
k_slip
Fx [N]
Fy [N]
Clean Noisy
Data analysis
Features selection
Data preprocessing
• Noisy signals • Quantization errors • Missing data
Random irrelevant patterns
ML Model : “Grey cars are very fast!”
Random irrelevant patterns
ML Model : “ ??? ”
CONVENTION DOESN’T EXIST
wheel rad
24,5[cm]9,65[inch]
330[km/h]205[mph]
BELFAGOR : OUR PREPROCESSING TOOL
Data analysis
Features selection
Data preprocessing
Samples distinguishibility
features nr.
Curse of dimensionality
Features ranking
Raw features
Engineers features
Scikit-Learn Chi2, Variance
Threshold, …
Scikit-Learn ensemble methods,
SVM
Wrappers features selection
Scikit-Learn metrics
Statistical features selection
Proprietary algorithms
Domain knowledge
Data analysis
Features selection
Data preprocessing
Data analysis
Features selection
Data preprocessing Wrappers
features selection SVM
Data analysis
Features selection
Data preprocessing
SVM example: Evaluate speed and steer signals as
features subset for Yaw Rate classification
✓
Data analysis
Features selection
Data preprocessing
SVM example: Evaluate speed and battery current
signals as features subset for Yaw Rate classification
✗
Model Selection
Params calibration Neural Networks
x1
x2∑ | yw2
w1
Neuron/Perceptron
Model Selection
Params calibration
Neural Networks example: Yaw Rate classification
x1
x2 y
h5
h4
h3
h2
h1
b1
b2
class 0 = yawr < -3 class 1 = yawr >=-3
Model Selection
Params calibration
Neural Networks example: Yaw Rate classification
class 0 = yawr < -3 class 1 = yawr >=-3
x = class 0 x = class 1
x = correct x = error
Labels Predictions
Deep Neural Networks
class = 1 class = 0
class = 1 f11 f10
class = 0 f01 f00
CONFUSION MATRIX
Predicted class
True class
Accuracy = # of correct predictions / # of predictions = (f11 + f00) / (f11 + f10 + f01 + f00)
Error rate = # of wrong predictions / # of predictions = (f10 + f01) / (f11 + f10 + f01 + f00)
RESULTS EVALUATION
WHERE WE WERE
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DISTORTION
“One Tool to rule them all, !
One Tool to find them,!
One Tool to bring them all !
and in the BlackBox correlate them”
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Adapter
DISTORTION MAP
Data Uploader
Job Manager
Worker[s]
Algorithms API
…
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JOB MANAGER
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JOB MANAGER
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WHY ?
RELATIONAL PL
OPEN TRIGGER
VIEW
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WHY PYTHON?
• it’s awesome
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E M B E D D E D
Resources Optimization Processor Specific Tuning Multi-Core & Polyedrical Optimization Microprocessors and FPGA Targets !SW in-the-loop HW in-the-loop
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WHAT’S FOR THE FUTURE…• Libraries versions management (e.g. ANACONDA virtual env.)
• Data/Results analysis tools
• More Design of Experiment
• Some technical details:
• preemption management
• data caching in worker module
• Suggestions?
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Questions?
it.linkedin.com/in/dani84bs/it
@Dani84bs
it.linkedin.com/pub/gloria-lovera/5b/152/4a8/
@LoveraGloria