I'm sorry if I miss something but I don't understand why this doesn't work:
using DataFrames, MLJ
julia> df = DataFrame(A = 1:4, B = ["M", "F", "F", "M"])
4×2 DataFrame
│ Row │ A │ B │
│ │ Int64 │ String │
├─────┼───────┼────────┤
│ 1 │ 1 │ M │
│ 2 │ 2 │ F │
│ 3 │ 3 │ F │
│ 4 │ 4 │ M │
julia> hot_model = OneHotEncoder()
julia> hot = machine(hot_model, df)
julia> fit!(hot)
julia> Xt = MLJ.transform(hot, df)
Xt is exacty as df, it didn't tranform the columns.
I tried to specify the features in OneHotEncoder() but it doesn't change.
I also saw that you can make a pipeline with it by wrapping it and fitting only at the end with the model but it should work like that, no? Is it maybe because of the type of the columns? What scitype should it be? Categorical? How can I change it into that?
Yes, you will need to change the scitypes of the columns. You can check the scitype of each column by using schema on the data frame:
julia> schema(df)
┌─────────┬─────────┬────────────┐
│ _.names │ _.types │ _.scitypes │
├─────────┼─────────┼────────────┤
│ A │ Int64 │ Count │
│ B │ String │ Textual │
└─────────┴─────────┴────────────┘
_.nrows = 4
Here you can see that the scitype of column B is Textual, so you will need to change that to Multiclass. You can use the coerce function to change the scitypes of the columns. Note that in MLJ integer columns are interpreted as count data, so you will also need to coerce column A if you want it to represent continuous data. The coerce method can be used as follows:
julia> coerce!(df, :A => Continuous, :B => Multiclass)
4×2 DataFrame
│ Row │ A │ B │
│ │ Float64 │ Cat… │
├─────┼─────────┼──────┤
│ 1 │ 1.0 │ M │
│ 2 │ 2.0 │ F │
│ 3 │ 3.0 │ F │
│ 4 │ 4.0 │ M │
Now the one-hot encoder will work properly.
ohe = machine(OneHotEncoder(), df)
fit!(ohe)
Xt = MLJ.transform(ohe, df)
4×3 DataFrame
│ Row │ A │ B__F │ B__M │
│ │ Float64 │ Float64 │ Float64 │
├─────┼─────────┼─────────┼─────────┤
│ 1 │ 1.0 │ 0.0 │ 1.0 │
│ 2 │ 2.0 │ 1.0 │ 0.0 │
│ 3 │ 3.0 │ 1.0 │ 0.0 │
│ 4 │ 4.0 │ 0.0 │ 1.0 │
See the section of the MLJ manual on working with categorical data for more information.
Related
I am employing MULTI-GPU training using pytorch lightning. The below output displays the model:
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]
┏━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ ┃ Name ┃ Type ┃ Params ┃
┡━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ 0 │ encoder │ Encoder │ 2.0 M │
│ 1 │ classifier │ Sequential │ 8.8 K │
│ 2 │ criterion │ BCEWithLogitsLoss │ 0 │
│ 3 │ train_acc │ Accuracy │ 0 │
│ 4 │ val_acc │ Accuracy │ 0 │
│ 5 │ train_auc │ AUROC │ 0 │
│ 6 │ val_auc │ AUROC │ 0 │
│ 7 │ train_f1 │ F1Score │ 0 │
│ 8 │ val_f1 │ F1Score │ 0 │
│ 9 │ train_mcc │ MatthewsCorrCoef │ 0 │
│ 10 │ val_mcc │ MatthewsCorrCoef │ 0 │
│ 11 │ train_sens │ Recall │ 0 │
│ 12 │ val_sens │ Recall │ 0 │
│ 13 │ train_spec │ Specificity │ 0 │
│ 14 │ val_spec │ Specificity │ 0 │
└────┴────────────┴───────────────────┴────────┘
Trainable params: 2.0 M
Non-trainable params: 0
I have set Encoder to be untrainable using the below code:
ckpt = torch.load(chk_path)
self.encoder.load_state_dict(ckpt['state_dict'])
self.encoder.requires_grad = False
Shouldn't trainable params be 8.8 K rather than 2.0 M ?
My optimizer is the following:
optimizer = torch.optim.RMSprop(filter(lambda p: p.requires_grad, self.parameters()), lr =self.lr, weight_decay = self.weight_decay)
self.encoder.requires_grad = False doesn't do anything; in fact, torch Modules don't have a requires_grad flag.
What you should do instead is use the requires_grad_ method (note the second underscore), that will set requires_grad for all the parameters of this module to the desired value:
self.encoder.requires_grad_(False)
as described here: https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.requires_grad_
You need to set requires_grad=False for all encoder parameters one-by-one:
for param in self.encoder.parameters():
param.requires_grad = False
Notice that if you execute the following piece of code:
class MNISTModel(LightningModule):
def __init__(self):
super().__init__()
self.l1 = torch.nn.Linear(28 * 28, 10)
def forward(self, x):
return torch.relu(self.l1(x.view(x.size(0), -1)))
def training_step(self, batch, batch_nb):
x, y = batch
loss = F.cross_entropy(self(x), y)
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.02)
mnist_model = MNISTModel()
mnist_model.l2.requires_grad = False
print(mnist_model.l2.weight.requires_grad)
print(mnist_model.l2.bias.requires_grad)
ModelSummary(mnist_model)
You will get:
True
True
| Name | Type | Params
--------------------------------
0 | l1 | Linear | 1.2 M
1 | l2 | Linear | 2.5 M
2 | l3 | Linear | 15.7 K
--------------------------------
3.7 M Trainable params
0 Non-trainable params
3.7 M Total params
14.827 Total estimated model params size (MB)
which means that this is actually not deactivating requires_grad for the parameters in that layer. So, you have two option according to (https://pytorch.org/docs/stable/notes/autograd.html#setting-requires-grad)
Applying .requires_grad_() to a module as suggested by #burzam (the more correct one)
mnist_model = MNISTModel()
mnist_model.l2.requires_grad_(False)
ModelSummary(mnist_model)
| Name | Type | Params
--------------------------------
0 | l1 | Linear | 1.2 M
1 | l2 | Linear | 2.5 M
2 | l3 | Linear | 15.7 K
--------------------------------
1.2 M Trainable params
2.5 M Non-trainable params
3.7 M Total params
14.827 Total estimated model params size (MB)
Loop through the parameters in the module
mnist_model = MNISTModel()
for param in mnist_model.l2.parameters():
param.requires_grad = False
ModelSummary(mnist_model)
you will see:
| Name | Type | Params
--------------------------------
0 | l1 | Linear | 1.2 M
1 | l2 | Linear | 2.5 M
2 | l3 | Linear | 15.7 K
--------------------------------
1.2 M Trainable params
2.5 M Non-trainable params
3.7 M Total params
14.827 Total estimated model params size (MB)
You need to to set requires_grad to False for all the parameters in the specific layers you want to deactivate
I have a julia TimeArray, let's say ta, and I want to build sub_array a TimeArray sub_ta by extracting some of the columns. Some month ago, I used a code similar to minimal example below, but which doesn't work anymore
import TimeSeries
import Dates
dates_index = [ Dates.Date(1970,1,day) for day in [1,2,3,4,5] ]
values = [ [1.0 2.0 3.0 4.0 5.0] ; [10.0 20.0 30.0 40.0 50.0] ; [ 100.0 200.0 300.0 400.0 500.0] ]
ta = TimeSeries.TimeArray( dates_index, transpose(values), [ :col1, :col2, :col3 ] )
sub_ta = ta[ [ :col1 , :col2 ] ]
ERROR: MethodError: no method matching getindex(::TimeSeries.TimeArray{Float64,2,Dates.Date,LinearAlgebra.Transpose{Float64,Array{Float64,2}}}, ::Array{Symbol,1})
Closest candidates are:
getindex(::TimeSeries.TimeArray, ::Integer) at /home/guilhem/.julia/packages/TimeSeries/bbwst/src/timearray.jl:259
getindex(::TimeSeries.TimeArray, ::UnitRange{#s30} where #s30<:Integer) at /home/guilhem/.julia/packages/TimeSeries/bbwst/src/timearray.jl:268
getindex(::TimeSeries.TimeArray, ::AbstractArray{#s30,1} where #s30<:Integer) at /home/guilhem/.julia/packages/TimeSeries/bbwst/src/timearray.jl:276
What seems strange to me is that there is, in the source of the TimeSeries library (in the file timearray.jl) a function getindex which should work if we want to work on many columns.
# array of columns by name
function getindex(ta::TimeArray, ss::Symbol...)
ns = [findcol(ta, s) for s in ss]
TimeArray(timestamp(ta), values(ta)[:, ns], collect(ss), meta(ta))
end
But I think I didn't get the proper way to use it, probably due to the splat operator what I don't really master
problem is both on julia-1.1.0 and julia-1.3.1, with TimeSeries v0.14.0
Finally I think I found the solution, I was quite close :
sub_ta = ta[ [:col1 , col2]...]
The best introduction I found on the ..., the splat operator is on this page (search "..." or "splat") :
enter link description here
What version of TimeSeries are you using?
(tmp) pkg> status
Status `/tmp/Project.toml`
[9e3dc215] TimeSeries v0.16.1
In version 0.16.1, both syntaxes that you mention seem to work:
julia> ta
5×3 TimeSeries.TimeArray{Float64,2,Dates.Date,LinearAlgebra.Transpose{Float64,Array{Float64,2}}} 1970-01-01 to 1970-01-05
│ │ col1 │ col2 │ col3 │
├────────────┼───────┼───────┼───────┤
│ 1970-01-01 │ 1.0 │ 10.0 │ 100.0 │
│ 1970-01-02 │ 2.0 │ 20.0 │ 200.0 │
│ 1970-01-03 │ 3.0 │ 30.0 │ 300.0 │
│ 1970-01-04 │ 4.0 │ 40.0 │ 400.0 │
│ 1970-01-05 │ 5.0 │ 50.0 │ 500.0 │
julia> ta[[:col1, :col2]]
5×2 TimeSeries.TimeArray{Float64,2,Dates.Date,Array{Float64,2}} 1970-01-01 to 1970-01-05
│ │ col1 │ col2 │
├────────────┼───────┼───────┤
│ 1970-01-01 │ 1.0 │ 10.0 │
│ 1970-01-02 │ 2.0 │ 20.0 │
│ 1970-01-03 │ 3.0 │ 30.0 │
│ 1970-01-04 │ 4.0 │ 40.0 │
│ 1970-01-05 │ 5.0 │ 50.0 │
julia> ta[[:col1, :col2]...]
5×2 TimeSeries.TimeArray{Float64,2,Dates.Date,Array{Float64,2}} 1970-01-01 to 1970-01-05
│ │ col1 │ col2 │
├────────────┼───────┼───────┤
│ 1970-01-01 │ 1.0 │ 10.0 │
│ 1970-01-02 │ 2.0 │ 20.0 │
│ 1970-01-03 │ 3.0 │ 30.0 │
│ 1970-01-04 │ 4.0 │ 40.0 │
│ 1970-01-05 │ 5.0 │ 50.0 │
Note that this last version is a rather convoluted way of writing ta[:col1, :col2]:
julia> ta[:col1, :col2]
5×2 TimeSeries.TimeArray{Float64,2,Dates.Date,Array{Float64,2}} 1970-01-01 to 1970-01-05
│ │ col1 │ col2 │
├────────────┼───────┼───────┤
│ 1970-01-01 │ 1.0 │ 10.0 │
│ 1970-01-02 │ 2.0 │ 20.0 │
│ 1970-01-03 │ 3.0 │ 30.0 │
│ 1970-01-04 │ 4.0 │ 40.0 │
│ 1970-01-05 │ 5.0 │ 50.0 │
Suppose there are 8 pcs and 1 switch, I want to divide three subnets.how to use alloy language program?Can you give an example?
The following models a small network.
sig IP {}
some sig Subnet {
range : some IP
}
abstract sig Node {
ips : some IP
}
sig Router extends Node {
subnets : IP -> lone Subnet
} {
ips = subnets.Subnet
all subnet : Subnet {
lone subnets.subnet
subnets.subnet in subnet.range
}
}
sig PC extends Node {} {
one ips
}
let routes = { disj s1, s2 : Subnet | some r : Router | s1+s2 in r.subnets[IP] }
let subnet[ip] = range.ip
let route[a,b] = subnet[a]->subnet[b] in ^ routes
fact NoOverlappingRanges { all ip : IP | one range.ip }
fact DHCP { all disj a, b : Node | no (a.ips & b.ips) }
fact Reachable { all disj a, b : IP | route[a,b] }
run {
# PC = 8
# Subnet = 3
# Router = 1
} for 12
If you run it:
┌───────────┬────────────┐
│this/Router│subnets │
├───────────┼────┬───────┤
│Router⁰ │IP² │Subnet¹│
│ ├────┼───────┤
│ │IP³ │Subnet⁰│
│ ├────┼───────┤
│ │IP¹¹│Subnet²│
└───────────┴────┴───────┘
┌───────────┬─────┐
│this/Subnet│range│
├───────────┼─────┤
│Subnet⁰ │IP³ │
│ ├─────┤
│ │IP⁴ │
├───────────┼─────┤
│Subnet¹ │IP¹ │
│ ├─────┤
│ │IP² │
│ ├─────┤
│ │IP⁵ │
│ ├─────┤
│ │IP⁶ │
│ ├─────┤
│ │IP⁷ │
│ ├─────┤
│ │IP⁸ │
│ ├─────┤
│ │IP⁹ │
│ ├─────┤
│ │IP¹⁰ │
├───────────┼─────┤
│Subnet² │IP⁰ │
│ ├─────┤
│ │IP¹¹ │
└───────────┴─────┘
┌─────────┬────┐
│this/Node│ips │
├─────────┼────┤
│PC⁰ │IP¹⁰│
├─────────┼────┤
│PC¹ │IP⁹ │
├─────────┼────┤
│PC² │IP⁸ │
├─────────┼────┤
│PC³ │IP⁷ │
├─────────┼────┤
│PC⁴ │IP⁶ │
├─────────┼────┤
│PC⁵ │IP⁵ │
├─────────┼────┤
│PC⁶ │IP⁴ │
├─────────┼────┤
│PC⁷ │IP¹ │
├─────────┼────┤
│Router⁰ │IP² │
│ ├────┤
│ │IP³ │
│ ├────┤
│ │IP¹¹│
└─────────┴────┘
You'd probably like to see what PCs are assigned to what subnet. Then go to the evaluator and type:
ips.~range
┌───────┬───────┐
│PC⁰ │Subnet¹│
├───────┼───────┤
│PC¹ │Subnet¹│
├───────┼───────┤
│PC² │Subnet¹│
├───────┼───────┤
│PC³ │Subnet¹│
├───────┼───────┤
│PC⁴ │Subnet¹│
├───────┼───────┤
│PC⁵ │Subnet¹│
├───────┼───────┤
│PC⁶ │Subnet⁰│
├───────┼───────┤
│PC⁷ │Subnet¹│
├───────┼───────┤
│Router⁰│Subnet⁰│
│ ├───────┤
│ │Subnet¹│
│ ├───────┤
│ │Subnet²│
└───────┴───────┘
Disclaimer: This was quickly hacked together so there might be modeling errors.
Alloy is a modelling language used mainly to reason about designs. So Forget about "programming".
What you can do in Alloy is to define the general rules of how pc, switch and subnets relate to each other. You can then verify if those rules allow to divide those pc into three subnets, and if the division match your expecations. In the case it does not, congrats, you have found a "bug" in your specification, solving it will improve your understanding of the constraints inherent to the system you are currently modelling.
I'd like to view the affected files and/or changes that will be made prior to running a method in my custom Rails generator. I've looked through the docs for days and am beginning to think its not possible.
module Mygem
module Generators
class InstallGenerator < Rails::Generators::Base
source_root File.expand_path('../templates', __FILE__)
def copy_theme_files_to_app
directory( source_paths[0] + "/mytemplate", Dir.pwd)
end
end
end
end
In the example above I'm trying to copy the contents of a template directory into the destination app.
├── lib
│ ├── generators
│ │ ├── mygem
│ │ │ ├── templates
│ │ │ │ ├── mytemplate
│ │ │ │ │ ├── app
│ │ │ │ │ │ ├── assets
│ │ │ │ │ │ │ ├── stylesheets
│ │ │ │ │ │ │ │ ├── application.scss
│ │ │ │ │ │ │ │ ├── custom.scss
Here are the contents of the "mytemplate" directory inside of my gem to give a little context. What I'm hoping to see inside of the generators copy_theme_files_to_app method is either an array of new paths to be generated/destroyed or showing the potential conflict between my template's application.scss file and the one in the app.
Is this possible?
I've got an iPad app which presents a modal view controller over the main view controller.
In the modal view controller, there is an image.
┌─────────────────────────────────┐
│ │
│ main VC in background │
│ │
│ ┌───────────────────┐ │
│ │ ┌─────────────┐ │ │
│ │ │ │ │ │
│ │ │ Image │ │ │
│ │ │ │ │ │
│ │ │ │ │ │
│ │ └─────────────┘ │ │
│ │ │ │
│ │ Modal VC │ │
│ │ │ │
│ │ │ │
└──────┴───────────────────┴──────┘
I want to be able to click the image to zoom the image to the size of the WHOLE SCREEN.
If I present the Zoom View Controller from the Modal View Controller, then it will retain the same size as the modal view:
┌─────────────────────────────────┐
│ │
│ main VC in background │
│ │
│ ┌───────────────────┐ │
│ │ │ │
│ │ │ │
│ │ Image │ │
│ │ │ │
│ │ │ │
│ │ │ │
│ ├───────────────────┤ │
│ │ │ │
│ │ Modal VC │ │
│ │ │ │
└──────┴───────────────────┴──────┘
...and if I try to present the Zoom View Controller from the Main View, I will get this error:
Warning: Attempt to present ZoomViewController on MainViewController which is already presenting ModalViewNavigationController
So what is the correct way to present a whole screen view controller over a modal view controller?
Present on the top most modal vc using the OverFullScreen presentation style. Basically present another modal from an already modally presented vc. It should be your ModalViewNavigationController?
What you want to do may not be possible. But you could work around it.
Do you really need to present modally at all? If there's nothing in main VC which a user can interact with, just present "modal VC" non-modally.
But if you do need to prevent users interacting with main VC's other views, you could first place a transparent view over it which covers the screen, preventing any interaction with the other views behind. Then present "modal VC" non-modally in front of that, and then your full screen image in front again.