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Machine Learning Introduction
October 20th 2016
Introduction
Johnny RAHAJARISON
@brainstorm_me
johnny.rahajarison@mylittleadventure.com
MyLittleAdventure
@mylitadventure
2
Agenda
What’s Machine Learning ?
Usage examples
Complexity
Algorithm families
Let’s go!
Troubleshoot
Tech insights
Next steps
Conclusion
3!
Machine learning
Introduction
4
What’s Machine Learning ?
Software that do something without being explicitly
programmed to, just by learning through examples
Same software can be used for various tasks
It learns from experiences with respect to some task and performance,
and improves through experience
5!
Usage examples (1/2)
6!
Some typical usage examples
Use cases : MyLittleAdventure (2/2)
7
Language detection
Clustering
Anomaly detection
Recommendation
Chose of parameters
MyLittleAdventure usage
!
Complexity
8!
"""Tests for convolution related functionality in tensorflow.ops.nn."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
class Conv2DTransposeTest(tf.test.TestCase):
def testConv2DTransposeSingleStride(self):
with self.test_session():
strides = [1, 1, 1, 1]
# Input, output: [batch, height, width, depth]
x_shape = [2, 6, 4, 3]
y_shape = [2, 6, 4, 2]
# Filter: [kernel_height, kernel_width, output_depth, input_depth]
f_shape = [3, 3, 2, 3]
x = tf.constant(1.0, shape=x_shape, name="x", dtype=tf.float32)
f = tf.constant(1.0, shape=f_shape, name="filter", dtype=tf.float32)
output = tf.nn.conv2d_transpose(x, f, y_shape, strides=strides,
padding="SAME")
value = output.eval()
# We count the number of cells being added at the locations in the output.
# At the center, #cells=kernel_height * kernel_width
# At the corners, #cells=ceil(kernel_height/2) * ceil(kernel_width/2)
# At the borders, #cells=ceil(kernel_height/2)*kernel_width or
# kernel_height * ceil(kernel_width/2)
for n in xrange(x_shape[0]):
for k in xrange(f_shape[2]):
for w in xrange(y_shape[2]):
for h in xrange(y_shape[1]):
target = 4 * 3.0
h_in = h > 0 and h < y_shape[1] - 1
w_in = w > 0 and w < y_shape[2] - 1
if h_in and w_in:
target += 5 * 3.0
"""GradientDescent for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
class GradientDescentOptimizer(optimizer.Optimizer):
"""Optimizer that implements the gradient descent algorithm.
@@__init__
"""
def __init__(self, learning_rate, use_locking=False, name="GradientDescent"):
"""Construct a new gradient descent optimizer.
Args:
learning_rate: A Tensor or a floating point value. The learning
rate to use.
use_locking: If True use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "GradientDescent".
"""
super(GradientDescentOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
def _apply_dense(self, grad, var):
return training_ops.apply_gradient_descent(
var,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
grad,
use_locking=self._use_locking).op
def _apply_sparse(self, grad, var):
delta = ops.IndexedSlices(
grad.values *
"""Tests for tensorflow.ops.linalg_grad."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class ShapeTest(tf.test.TestCase):
def testBatchGradientUnknownSize(self):
with self.test_session():
batch_size = tf.constant(3)
matrix_size = tf.constant(4)
batch_identity = tf.tile(
tf.expand_dims(
tf.diag(tf.ones([matrix_size])), 0), [batch_size, 1, 1])
determinants = tf.matrix_determinant(batch_identity)
reduced = tf.reduce_sum(determinants)
sum_grad = tf.gradients(reduced, batch_identity)[0]
self.assertAllClose(batch_identity.eval(), sum_grad.eval())
class MatrixUnaryFunctorGradientTest(tf.test.TestCase):
pass # Filled in below
def _GetMatrixUnaryFunctorGradientTest(functor_, dtype_, shape_, **kwargs_):
def Test(self):
with self.test_session():
np.random.seed(1)
m = np.random.uniform(low=-1.0,
high=1.0,
size=np.prod(shape_)).reshape(shape_).astype(dtype_)
a = tf.constant(m)
b = functor_(a, **kwargs_)
# Optimal stepsize for central difference is O(epsilon^{1/3}).
epsilon = np.finfo(dtype_).eps
delta = 0.1 * epsilon**(1.0 / 3.0)
# tolerance obtained by looking at actual differences using
# np.linalg.norm(theoretical-numerical, np.inf) on -mavx build
Complex algorithm before
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
And Machine learning now…
Machine learning
Algorithm families
9
Supervised algorithms
10
Supervised algorithms
ClassificationRegression
Unsupervised algorithms
11
Unsupervised algorithms
ClusteringAnomaly detection
Machine learning
Let’s go!
12
Recipe
!13
Collect
Training data
Files, database,
cache, data flow
Selection of model,
and (hyper) parameters
Train algorithm
Use or store your
trained estimator
Make predictions
Measure accuracy
precision
Measure
Collect training data
Get qualitative data
Get some samples
Don’t get data for months and then try
Go fast and try things.
14
Weight (g) Width (cm) Height (cm) Label
192 8.4 7.3 Granny smith apple
86 6.2 4.7 Mandarin
178 7.1 7.8 Braeburn apple
162 7.4 7.2 Cripps pink apple
118 6.1 8.1 Unidentified lemons
144 6.8 7.4 Turkey orange
362 9.6 9.2 Spanish jumbo orange
… … … …
What about the data?
Fruit identification example
Prepare your data
15
Numerize your features and labels
Put them in same scale (normalization) ?
Weight (g) Width (cm) Height (cm) Label
192 8.4 7.3 1
86 6.2 4.7 2
178 7.1 7.8 3
162 7.4 7.2 5
118 6.1 8.1 10
144 6.8 7.4 8
362 9.6 9.2 9
… … … …
We need to have some tests
Training set
Learning phase (60% - 80 %)
Test set
Analytics phase (20% - 40%)
16
Prepare your data (code)
Train algorithm
17
Choose a classifier
Fit the decision tree
Weight (g) Width (cm) Height (cm) Label
192 8.4 7.3 1
86 6.2 4.7 2
178 7.1 7.8 3
162 7.4 7.2 5
118 6.1 8.1 10
144 6.8 7.4 8
362 9.6 9.2 9
… … … …
We need to choose an estimator
Make predictions
18
What looks our predictions?
Weight
(g)
Width
(cm)
Height
(cm)
Label
192 8.4 7.3 1
86 6.2 4.7 2
178 7.1 7.8 3
Test set
Weight
(g)
Width
(cm)
Height
(cm)
Label
192 8.4 7.3 1
86 6.2 4.7 2
178 7.1 7.8 1
Predictions
!
Measure (1/2)
Evaluate on the dataset that as never ever been learned by your model
19!
Accuracy
Correct predictions / total predictions
Gives a simple confidence score of our performance level
Measure (2/2)
Try to visualize and analyze your data, and know what you want
20!
Actual true Actual false
Predicted
true
True
positive
False
positive
Predicted
false
False
negative
True
negative
Confusion Matrix
Skewed classes
Precision = True positives / #predicted positives
Recall = True positives / #actual positives
F1 score (trade-off) = (precision * recall) /
(precision + recall)
21
Measure and prediction (code)
Machine learning
Troubleshoot
22
Troubleshoot (1/4)
23!
Under/Overfitting situation
Troubleshoot (2/4)
Underfitting
Add / create more features
Use more sophisticated model
Use fewer samples
Decrease regularization
24!
Overfitting
Use fewer features
Use more simple model
Use more samples
Increase regularization
What are the different options ?
Troubleshoot (3/4)
25!
Underfitting Overfitting
Using the learning curves…
Troubleshoot : Model choice (4/4)
26!
Machine learning
Tech insights
27
Platforms : easy, peasy
You don’t even have to code to build something
(*wink wink* business developers)
Built-in models
Data munging
Model management by UI
PaaS
28!
Very high-level solutions
Languages
For understanding &
prototyping implementation
Most Valuable Languages
Comfortable for prototyping,
yet powerful for
industrialisation
For bigger companies &
projects, and fine-tuned
softwares
29!
Matlab Octave Go Python Java C++
What language for what purpose ?
Libraries
Built-in models
Data munging
Fine-tuning
Full integration to your product
30!
You will have great power using a library
Golearn
Machine learning
Next steps…
31
Next steps
Split your data in 3 : Training / Cross validation / Test set
Know the top algorithms
Search advanced techniques and optimizers (online learning, stacking)
Deep and reinforcement learning
Partial and semi-supervised learning
Transfer learning
How to store and analyse big data ? How do we scale ?
!32
Try it ! Find your best tools and have some fun
Conclusion
Try it and let’s get in touch!
Machine learning is not just a buzz word
Difficulties are not always what we think!
Machine learning is rather experiences and tests than just algorithms
There is no perfect unique solution
There is plenty of easy to use solutions for beginners
33!
Machine learning
One more thing!
34
Tensorflow
35
Tensorflow learn
36
Thank you
Machine Learning Introduction
October 20th 2016
Questions ?

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Introduction Machine Learning by MyLittleAdventure

  • 1. B2B Travel technology Machine Learning Introduction October 20th 2016
  • 3. Agenda What’s Machine Learning ? Usage examples Complexity Algorithm families Let’s go! Troubleshoot Tech insights Next steps Conclusion 3!
  • 5. What’s Machine Learning ? Software that do something without being explicitly programmed to, just by learning through examples Same software can be used for various tasks It learns from experiences with respect to some task and performance, and improves through experience 5!
  • 6. Usage examples (1/2) 6! Some typical usage examples
  • 7. Use cases : MyLittleAdventure (2/2) 7 Language detection Clustering Anomaly detection Recommendation Chose of parameters MyLittleAdventure usage !
  • 8. Complexity 8! """Tests for convolution related functionality in tensorflow.ops.nn.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin import tensorflow as tf class Conv2DTransposeTest(tf.test.TestCase): def testConv2DTransposeSingleStride(self): with self.test_session(): strides = [1, 1, 1, 1] # Input, output: [batch, height, width, depth] x_shape = [2, 6, 4, 3] y_shape = [2, 6, 4, 2] # Filter: [kernel_height, kernel_width, output_depth, input_depth] f_shape = [3, 3, 2, 3] x = tf.constant(1.0, shape=x_shape, name="x", dtype=tf.float32) f = tf.constant(1.0, shape=f_shape, name="filter", dtype=tf.float32) output = tf.nn.conv2d_transpose(x, f, y_shape, strides=strides, padding="SAME") value = output.eval() # We count the number of cells being added at the locations in the output. # At the center, #cells=kernel_height * kernel_width # At the corners, #cells=ceil(kernel_height/2) * ceil(kernel_width/2) # At the borders, #cells=ceil(kernel_height/2)*kernel_width or # kernel_height * ceil(kernel_width/2) for n in xrange(x_shape[0]): for k in xrange(f_shape[2]): for w in xrange(y_shape[2]): for h in xrange(y_shape[1]): target = 4 * 3.0 h_in = h > 0 and h < y_shape[1] - 1 w_in = w > 0 and w < y_shape[2] - 1 if h_in and w_in: target += 5 * 3.0 """GradientDescent for TensorFlow.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops from tensorflow.python.training import optimizer from tensorflow.python.training import training_ops class GradientDescentOptimizer(optimizer.Optimizer): """Optimizer that implements the gradient descent algorithm. @@__init__ """ def __init__(self, learning_rate, use_locking=False, name="GradientDescent"): """Construct a new gradient descent optimizer. Args: learning_rate: A Tensor or a floating point value. The learning rate to use. use_locking: If True use locks for update operations. name: Optional name prefix for the operations created when applying gradients. Defaults to "GradientDescent". """ super(GradientDescentOptimizer, self).__init__(use_locking, name) self._learning_rate = learning_rate def _apply_dense(self, grad, var): return training_ops.apply_gradient_descent( var, math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype), grad, use_locking=self._use_locking).op def _apply_sparse(self, grad, var): delta = ops.IndexedSlices( grad.values * """Tests for tensorflow.ops.linalg_grad.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow as tf class ShapeTest(tf.test.TestCase): def testBatchGradientUnknownSize(self): with self.test_session(): batch_size = tf.constant(3) matrix_size = tf.constant(4) batch_identity = tf.tile( tf.expand_dims( tf.diag(tf.ones([matrix_size])), 0), [batch_size, 1, 1]) determinants = tf.matrix_determinant(batch_identity) reduced = tf.reduce_sum(determinants) sum_grad = tf.gradients(reduced, batch_identity)[0] self.assertAllClose(batch_identity.eval(), sum_grad.eval()) class MatrixUnaryFunctorGradientTest(tf.test.TestCase): pass # Filled in below def _GetMatrixUnaryFunctorGradientTest(functor_, dtype_, shape_, **kwargs_): def Test(self): with self.test_session(): np.random.seed(1) m = np.random.uniform(low=-1.0, high=1.0, size=np.prod(shape_)).reshape(shape_).astype(dtype_) a = tf.constant(m) b = functor_(a, **kwargs_) # Optimal stepsize for central difference is O(epsilon^{1/3}). epsilon = np.finfo(dtype_).eps delta = 0.1 * epsilon**(1.0 / 3.0) # tolerance obtained by looking at actual differences using # np.linalg.norm(theoretical-numerical, np.inf) on -mavx build Complex algorithm before train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) And Machine learning now…
  • 13. Recipe !13 Collect Training data Files, database, cache, data flow Selection of model, and (hyper) parameters Train algorithm Use or store your trained estimator Make predictions Measure accuracy precision Measure
  • 14. Collect training data Get qualitative data Get some samples Don’t get data for months and then try Go fast and try things. 14 Weight (g) Width (cm) Height (cm) Label 192 8.4 7.3 Granny smith apple 86 6.2 4.7 Mandarin 178 7.1 7.8 Braeburn apple 162 7.4 7.2 Cripps pink apple 118 6.1 8.1 Unidentified lemons 144 6.8 7.4 Turkey orange 362 9.6 9.2 Spanish jumbo orange … … … … What about the data? Fruit identification example
  • 15. Prepare your data 15 Numerize your features and labels Put them in same scale (normalization) ? Weight (g) Width (cm) Height (cm) Label 192 8.4 7.3 1 86 6.2 4.7 2 178 7.1 7.8 3 162 7.4 7.2 5 118 6.1 8.1 10 144 6.8 7.4 8 362 9.6 9.2 9 … … … … We need to have some tests Training set Learning phase (60% - 80 %) Test set Analytics phase (20% - 40%)
  • 17. Train algorithm 17 Choose a classifier Fit the decision tree Weight (g) Width (cm) Height (cm) Label 192 8.4 7.3 1 86 6.2 4.7 2 178 7.1 7.8 3 162 7.4 7.2 5 118 6.1 8.1 10 144 6.8 7.4 8 362 9.6 9.2 9 … … … … We need to choose an estimator
  • 18. Make predictions 18 What looks our predictions? Weight (g) Width (cm) Height (cm) Label 192 8.4 7.3 1 86 6.2 4.7 2 178 7.1 7.8 3 Test set Weight (g) Width (cm) Height (cm) Label 192 8.4 7.3 1 86 6.2 4.7 2 178 7.1 7.8 1 Predictions !
  • 19. Measure (1/2) Evaluate on the dataset that as never ever been learned by your model 19! Accuracy Correct predictions / total predictions Gives a simple confidence score of our performance level
  • 20. Measure (2/2) Try to visualize and analyze your data, and know what you want 20! Actual true Actual false Predicted true True positive False positive Predicted false False negative True negative Confusion Matrix Skewed classes Precision = True positives / #predicted positives Recall = True positives / #actual positives F1 score (trade-off) = (precision * recall) / (precision + recall)
  • 24. Troubleshoot (2/4) Underfitting Add / create more features Use more sophisticated model Use fewer samples Decrease regularization 24! Overfitting Use fewer features Use more simple model Use more samples Increase regularization What are the different options ?
  • 26. Troubleshoot : Model choice (4/4) 26!
  • 28. Platforms : easy, peasy You don’t even have to code to build something (*wink wink* business developers) Built-in models Data munging Model management by UI PaaS 28! Very high-level solutions
  • 29. Languages For understanding & prototyping implementation Most Valuable Languages Comfortable for prototyping, yet powerful for industrialisation For bigger companies & projects, and fine-tuned softwares 29! Matlab Octave Go Python Java C++ What language for what purpose ?
  • 30. Libraries Built-in models Data munging Fine-tuning Full integration to your product 30! You will have great power using a library Golearn
  • 32. Next steps Split your data in 3 : Training / Cross validation / Test set Know the top algorithms Search advanced techniques and optimizers (online learning, stacking) Deep and reinforcement learning Partial and semi-supervised learning Transfer learning How to store and analyse big data ? How do we scale ? !32 Try it ! Find your best tools and have some fun
  • 33. Conclusion Try it and let’s get in touch! Machine learning is not just a buzz word Difficulties are not always what we think! Machine learning is rather experiences and tests than just algorithms There is no perfect unique solution There is plenty of easy to use solutions for beginners 33!
  • 37. Thank you Machine Learning Introduction October 20th 2016 Questions ?