1. General pursuit of nonlinearity

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• We have seen how we can inject nonlinear functions into supervised/unsupervised

• In general we cannot determine what sort of nonliearity to use for a given dataset

• So we try various combinations of different generic nonlinear functions, and see which combination works best

• We need to be organized and discipline in this search

• Simply taking random combinations of various functions does not allow for effecient computation or fine-tuning of nonlinearity

• Conversely if we retrict ourselves to using a set of related functions we can better manage, optimize, and perform the task

Example 1. A combination of very different functions is difficult to use

• Suppose we have a regression dataset where we cannot guess (via visualization or intuition) a good nonlinearity

• We use a familiar toy data below to simulate our task of searching over various combinations of nonlinear functions to find a fitting nonlinear representation for the data

# create instance of linear regression demo, used below and in the next examples
csvname =  datapath + 'noisy_sin_sample.csv'
demo1 = nonlib.demos_part_2.Visualizer(csvname)
demo1.show_pts()

• Say we limit ourselves to trying linear combinations of the following three functions

\begin{equation} f_1(x) = x ~~~~~~~~~~~~~~ f_2(x) = x^2 ~~~~~~~~~~~~~~ f_3(x) = \text{sinc}(10x) \end{equation}

# plot our features
demo1.plot_feats(version = 1)

• We first try

\begin{equation} \text{model}(x,\mathbf{w}) = w_0 + w_1f_1(x) \end{equation}

• then

\begin{equation} \text{model}(x,\mathbf{w}) = w_0 + w_1\,f_1(x) + w_2\,f_2(x) \end{equation}

• and finally

\begin{equation} \text{model}(x,\mathbf{w}) = w_0 + w_1\,f_1(x) + w_2\,f_2(x) + w_3\,f_3(x) \end{equation}

# show first round of fits
demo1.show_fits(version = 1)

• Here the third function is wildly different than the first two, we are unable to control gradual change in nonlinearity

• Now lets try same experiment but with all three functions related --> polynomials

\begin{equation} f_1(x) = x ~~~~~~~~~~~~~~ f_2(x) = x^2 ~~~~~~~~~~~~~~ f_3(x) = x^3 \end{equation}

# plot our features
demo1.plot_feats(version = 2)

# show first round of fits
demo1.show_fits(version = 2)

• We get a better fit of course, but the change in nonlinearity is more gradual, predictable, and controllable

The three standard species of functions used for prediction

• We could try adding various random functions for our nonlinear predictor e.g.,

\begin{equation} \text{model}(x,\mathbf{w}) = w_0 + w_1\text{tanh}\left(w_2 + w_3x\right) + w_4e^{w_5x} + w_6x^{10} + \cdots \end{equation}

• But to make computationally effecient search methods we need to be more organized than this

• Hence in practice we typically stick to combining functions from a single species - or collection of similar behaving functions

• We use $B$ functions from such a species, making a model function of the form

\begin{equation} \text{model}(x,\mathbf{w}) = w_0 + w_1\,f_1(x) + w_2\,f_2(x) + w_3\,f_3(x) + \cdots + w_B\,f_B(x) \end{equation}

• By adding together such functions we can create any sort of non-linearity we want, easier to control and optimize

• At a high level: three main types of species used in practice - kernels, neural networks, and trees

• Inside each catalog there are many sub-species, here we introduce each species via a simple but common example from each

• We will then circle back to discuss more complex sub-species of neural networks

1. Kernels

• Basic example: polynomials

\begin{equation} f_1(x) = x^1, ~~ f_2(x) = x^2, ~~ f_3(x) = x^3, ~~ f_4(x) = x^4, ... \end{equation}

# import Draw_Bases class for visualizing various basis element types
demo = nonlib.DrawBases.Visualizer()

# plot the first 4 elements of the polynomial basis
demo.show_1d_poly()

demo = nonlib.regression_basis_single.Visualizer()
csvname =  datapath + 'noisy_sin_sample.csv'
demo.load_data(csvname)
demo.brows_single_fit(basis='poly',num_units = [v for v in range(1,15)])

• For two inputs - $x_1$ and $x_2$ - the catalog of polynomial elements looks like

\begin{equation} f_1(x_1,x_2) = x_1, ~~ f_2(x_1,x_2) = x_2^2, ~~ f(x_1,x_2) = x_1x_2^3, ~~ f(x_1,x_2) = x_1^4x_2^6,... \end{equation}

• In general a degree $D$ element takes the form

\begin{equation} f_m(x_1,x_2) = x_1^px_2^q \end{equation}

where where $p$ and $q$ are nonnegative integers and $p + q \leq D$

demo.show_2d_poly()

demo = nonlib.regression_basis_comparison_3d.Visualizer()
csvname =  datapath + '3d_noisy_sin_sample.csv'
demo.load_data(csvname)
demo.brows_single_fits(num_elements =  [v for v in range(1,10)] ,view = [20,110],basis = 'poly')

• Can be defined likewise for general $N$ input dimension

• General attributes of kernel bases:
  • classic examples also include: Fourier and cosine bases, radial basis functions
  • each element has a fixed shape (no internal parameters), elements are ordered from simple to complex
  • always leads to convex cost functions for regression / classification
  • great for medium sized problems, but many varieties have serious scaling issues when $N$ and $P$ are large
  • often used with support vector machines (for historical reasons)

2. Neural networks

• Basic example: single hidden layer network with tanh activation

\begin{equation} f_1(x) = \text{tanh}\left(w_{1,0} + w_{1,1}x\right), ~~ f_2(x) = \text{tanh}\left(w_{2,0} + w_{2,1}x\right), ~~ f_3(x) = \text{tanh}\left(w_{3,0} + w_{3,1}x\right), ~~ f_4(x) = \text{tanh}\left(w_{4,0} + w_{4,1}x\right), ... \end{equation}

• Internal parameters make each function flexible

• e.g., take $f_1$ with various internal parameter settings

# import Draw_Bases class for visualizing various basis element types
demo = nonlib.DrawBases.Visualizer()

# plot the first 4 elements of the polynomial basis
demo.show_1d_net(num_layers = 1,activation = 'tanh')

• Basic example: single hidden layer network with relu activation

\begin{equation} f_1(x) = \text{max}\left(0,w_{1,0} + w_{1,1}x\right), ~~ f_2(x) = \text{max}\left(0,w_{2,0} + w_{2,1}x\right), ~~ f_3(x) = \text{max}\left(0,w_{3,0} + w_{3,1}x\right), ~~ f_4(x) = \text{max}\left(0,w_{4,0} + w_{4,1}x\right), ... \end{equation}

• Internal parameters make each function flexible

# import Draw_Bases class for visualizing various basis element types
demo = nonlib.DrawBases.Visualizer()

# plot the first 4 elements of the polynomial basis
demo.show_1d_net(num_layers = 1,activation = 'relu')

demo = nonlib.regression_basis_single.Visualizer()
csvname = datapath + 'noisy_sin_sample.csv'
demo.load_data(csvname)
demo.brows_single_fit(basis='tanh',num_units = [v for v in range(1,15)])

• For two inputs - $x_1$ and $x_2$ - the catalog of single layer functions with relu activation

\begin{equation} f_1(x) = \text{tanh}\left(w_{1,0} + w_{1,1}x_1 + w_{1,2}x_2\right), ~~ f_2(x) = \text{tanh}\left(w_{2,0} + w_{2,1}x_1 + w_{2,2}x_2\right) ... \end{equation}

• or likewise

\begin{equation} f_1(x) = \text{max}\left(0,w_{1,0} + w_{1,1}x_1 + w_{1,2}x_2\right), ~~ f_2(x) = \text{max}\left(0,w_{2,0} + w_{2,1}x_1 + w_{2,2}x_2\right) ... \end{equation}

demo = nonlib.regression_basis_comparison_3d.Visualizer()
csvname = datapath + '3d_noisy_sin_sample.csv'
demo.load_data(csvname)
demo.brows_single_fits(num_units =  [v for v in range(1,12)] ,view = [20,110],basis = 'net')

• Generalizes to $N$ dimensional input

• General attributes of neural network bases:
  • elements are of the same general type, each has adjustable shape (with internal parameters)
  • leads to non-convex cost functions for regression / classification
  • especially great for large high dimensional input problems
  • often used with logistic regression (for historical reasons)

3. Trees

• Basic example: depth-1 tree, also known as a stump

\begin{equation} f_1(x) = \begin{cases} x < V_1 \,\,\,\,\, a_1 \\ x \geq V_1 \,\,\,\,\, b_1 \end{cases} ~~~~~~~~ f_2(x) = \begin{cases} x < V_2 \,\,\,\,\, a_2 \\ x \geq V_2 \,\,\,\,\, b_2 \end{cases} ~~ \cdots \end{equation}

• Vocab: for $f_j$ the value $V_j$ called a split point, $a_j$ and $b_j$ called levels

# import Draw_Bases class for visualizing various basis element types
demo = nonlib.DrawBases.Visualizer()

# plot the first 4 elements of the polynomial basis
demo.show_1d_tree(depth = 1)

• Split points and levels set directly using data, not internal parameters

• e.g., a split point placed between two inputs, left / right level set to average of output to the left / right

demo = nonlib.stump_visualizer_2d.Visualizer()
csvname = datapath + 'noisy_sin_raised.csv'
demo.load_data(csvname)
demo.browse_stumps()

demo = nonlib.regression_basis_single.Visualizer()
csvname = datapath + 'noisy_sin_sample.csv'
demo.load_data(csvname)
demo.brows_single_fit(basis='tree',num_elements = [v for v in range(1,10)])

demo = nonlib.regression_basis_comparison_3d.Visualizer()
csvname = datapath + '3d_noisy_sin_sample.csv'
demo.load_data(csvname)
demo.brows_single_fits(num_elements =  [v for v in range(1,20)] ,view = [20,110],basis = 'tree')

• Generalizes to $N$ dimensional input (split points / levels in each individual dimension)

• General attributes of tree bases:
  • each element is adjustable, with internal parameters pre-set greedily to data
  • leads to convex cost functions for regression / classification, trained via boosting (greedy coordinate descent)
  • great for large sparse datasets

How many elements to choose?

• The more elements we use the better we fit our data (provided we optimize correctly)

• This is good in theory, but not in practice

• In theory -with infinite clean data and ininite computation - the more elements (of any basis) --> better fit / separation

• In fact: in such an instance all three catalogues equal, and can perfectly represent / separate

demo = nonlib.regression_basis_comparison_2d.Visualizer()
csvname = datapath + 'sin_function.csv'
demo.load_data(csvname)
demo.brows_fits(num_elements = [v for v in range(1,50,1)])

• With real data however more elements $\neq$ better fit

demo = nonlib.regression_basis_comparison_2d.Visualizer()
csvname = datapath + 'noisy_sin_sample.csv'
demo.load_data(csvname)
demo.brows_fits(num_elements = [v for v in range(1,25)])

• Nothing has 'gone wrong' here - we just do not know ahead of time how many elements is 'too little' and how many is 'too much' (in terms of nonlinearity)

• i.e., error on training set always goes down as we add elements (provided we optimize correctly)

demo = nonlib.regression_basis_single.Visualizer()
csvname = datapath + 'noisy_sin_sample.csv'
demo.load_data(csvname)
demo.brows_single_fit(basis='poly',num_elements = [v for v in range(1,25)])

• When fit is not nonlinear enough / we use too few basis features --> call this underfitting

• When fit is too nonlinear / we use too many basis features --> call this overfitting

• The way of finding best combination of elements to use called cross-validation