May i ask how to use function GraphLearner$new to transform in r-drake.
The codes following:
learner_plan = drake_plan(
## 1. Choose learner--------------------------------------------------------
polrn_classif = target(fnc_po_learner(list_learners),
transform = map(
list_learners = c(
"classif.ranger",
"classif.lightgbm",
"classif.xgboost",
"classif.log_reg",
"classif.svm"
)
)),
graph_classif = target(
imputer_classif %>>% filter_classif %>>% polrn_classif,
transform = map(polrn_classif)
),
glrn_classif = target({
GraphLearner$new(graph = graph_classif) # get error ---------- it's not transform
transform = map(graph_classif)
})
)
learner_plan
transform = map(graph_classif) should go outside the curly braces.
library(drake)
learner_plan = drake_plan(
polrn_classif = target(
fnc_po_learner(list_learners),
transform = map(
list_learners = c(
"classif.ranger",
"classif.lightgbm",
"classif.xgboost",
"classif.log_reg",
"classif.svm"
)
)
),
graph_classif = target(
imputer_classif %>>% filter_classif %>>% polrn_classif,
transform = map(polrn_classif)
),
glrn_classif = target({
GraphLearner$new(graph = graph_classif)
}, transform = map(graph_classif)) # outside the curly braces
)
plot(learner_plan)
Created on 2021-07-07 by the reprex package (v2.0.0)
Related
I’m trying to perform model interpretability with captum but running into an error. Specifically, it says:
/usr/lib/python3.7/inspect.py in _signature_from_callable(obj, follow_wrapper_chains, skip_bound_arg, sigcls)
2206
2207 if not callable(obj):
-> 2208 raise TypeError('{!r} is not a callable object'.format(obj))
2209
2210 if isinstance(obj, types.MethodType):
I’m not certain how to resolve this. Here’s the definition of my model, for reference:
class dvib(nn.Module):
def __init__(self,k,out_channels, hidden_size):
super(dvib, self).__init__()
self.conv = torch.nn.Conv2d(in_channels=1,
out_channels = out_channels,
kernel_size = (1,20),
stride=(1,1),
padding=(0,0),
)
self.rnn = torch.nn.GRU(input_size = out_channels,
hidden_size = hidden_size,
num_layers = 2,
bidirectional = True,
batch_first = True,
dropout = 0.2
)
self.fc1 = nn.Linear(hidden_size*4, hidden_size*4)
self.enc_mean = nn.Linear(hidden_size*4+578,k)
self.enc_std = nn.Linear(hidden_size*4+578,k)
self.dec = nn.Linear(k, 2)
nn.init.xavier_uniform_(self.fc1.weight)
nn.init.constant_(self.fc1.bias, 0.0)
nn.init.xavier_uniform_(self.enc_mean.weight)
nn.init.constant_(self.enc_mean.bias, 0.0)
nn.init.xavier_uniform_(self.enc_std.weight)
nn.init.constant_(self.enc_std.bias, 0.0)
nn.init.xavier_uniform_(self.dec.weight)
nn.init.constant_(self.dec.bias, 0.0)
def cnn_gru(self,x,lens):
print(x.shape)
x = x.unsqueeze(1)
print('after first unsqueeze: ', x.shape)
x = self.conv(x)
print('after conv: ', x.shape)
x = torch.nn.ReLU()(x)
print('shape after relu: ', x.shape,type(x))
x = x.squeeze(3)
print('shape after squeeze: ', x.shape)
x = x.view(x.size(0),-1)
x = x.permute(0,2,1)
print('shape after permute: ', x.shape)
print(type(lens))
gru_input = pack_padded_sequence(x,lens,batch_first=True, enforce_sorted=False)
output, hidden = self.rnn(gru_input)
print('hidden layer: ', hidden.shape)
output_all = torch.cat([hidden[-1],hidden[-2],hidden[-3],hidden[-4]],dim=1)
print("output_all.shape:",output_all.shape)
return output_all
def forward(self, pssm, lengths, FEGS):
cnn_vectors = self.cnn_gru(pssm, lengths)
feature_vec = torch.cat([cnn_vectors, FEGS], dim = 1)
enc_mean, enc_std = self.enc_mean(feature_vec), f.softplus(self.enc_std(feature_vec)-5)
eps = torch.randn_like(enc_std)
latent = enc_mean + enc_std*eps
outputs = f.sigmoid(self.dec(latent))
print(outputs.shape)
return outputs, enc_mean, enc_std, latent
I load pretrained weights into the model as well, prior to passing it to captum with the relevant arguments:
ig = IntegratedGradients(model(test_pssm_small, test_len_small, test_FEGS_small))
attr = ig.attribute(test_FEGS_small, n_steps=5)
I have illustrated the parallelogram spanned by two vectors, and would like to shade in the area of that parallelogram, which I tried to do like so:
from manim import *
import numpy as np
class DrawParallelogram( Scene ):
def construct( self ):
o = np.array( [ 0, -2, 0 ] )
p1 = np.array( [ 3, 1, 0 ] ) # u
p2 = np.array( [ 1, 3, 0 ] ) # v
op1 = o + p1
op2 = o + p2
op3 = o + p1 + p2
v1 = Arrow( start = o, end = op1, buff = 0, color = RED ) # u
v2 = Arrow( start = o, end = op2, buff = 0, color = YELLOW ) # v
v1p = Arrow( start = op2, end = op3, buff = 0, color = RED ) # u'
v2p = Arrow( start = op1, end = op3, buff = 0, color = YELLOW ) # v'
parallelogram = [ o, op1, op3, op2 ]
poly = Polygon( *parallelogram, color = PURPLE, fill_opacity = 0.5 )
self.play( AnimationGroup( Write( v1 ), Write( v2 ), Write( v1p ), Write( v2p ) ) )
self.wait( )
self.play( Write( poly ) )
However, this parallelogram colors over the arrows that I have already drawn, like so:
and I'd like it to be in the background. Is there a way to introduce a new object into the scene so that it is logically behind any of the existing ones, as if I had drawn it first, so that it would look like:
You can use the set_z_index method to set the z_index property of the parallelogram to a value less than that of the arrows.
Here I have set it to a lower value than that of v1:
poly.set_z_index(v1.z_index - 1)
Alternatively you can manipulate the z_index property directly:
poly.z_index = v1.z_index - 1
Using the set_z_index method would be the cleaner solution.
Complete code:
from manim import *
import numpy as np
class DrawParallelogram( Scene ):
def construct( self ):
o = np.array( [ 0, -2, 0 ] )
p1 = np.array( [ 3, 1, 0 ] ) # u
p2 = np.array( [ 1, 3, 0 ] ) # v
op1 = o + p1
op2 = o + p2
op3 = o + p1 + p2
v1 = Arrow( start = o, end = op1, buff = 0, color = RED ) # u
v2 = Arrow( start = o, end = op2, buff = 0, color = YELLOW ) # v
v1p = Arrow( start = op2, end = op3, buff = 0, color = RED ) # u'
v2p = Arrow( start = op1, end = op3, buff = 0, color = YELLOW ) # v'
parallelogram = [ o, op1, op3, op2 ]
poly = Polygon( *parallelogram, color = PURPLE, fill_opacity = 0.5 )
# Set the z-index
poly.set_z_index(v1.z_index - 1)
self.play( AnimationGroup( Write( v1 ), Write( v2 ), Write( v1p ), Write( v2p ) ) )
self.wait( )
self.play( Write( poly ) )
Is it possible to use a map transformation with a grouping variable that is described in an external plan?
In other words, this works for me:
plan_a = drake_plan(
foo = target(x + 1, transform = map(x = c(4, 5, 6))),
bar = target(y + 5, transform = map(foo))
)
but this doesn't:
plan_a = drake_plan(
foo = target(x + 1, transform = map(x = c(4, 5, 6))),
)
plan_b = drake_plan(
bar = target(y + 5, transform = map(foo))
)
bind_plans(plan_a, plan_b)
Thanks!
I found a solution using the transform_plan function.
I would like to create a flowchart using the DiagrammeR nodes and edges functionality with R instead of using the graphviz wrapper function.
However, I can't figure out how to make the edges straight to make it nice.
This is the graphviz solution that looks like what I want:
# Packages needed for the test
library(DiagrammeR)
# grViz solution
grViz("digraph flowchart {
# node definitions with substituted label text
node [fontname = Helvetica, shape = rectangle]
tab1 [label = '##1', group=gr1]
tab2 [label = '##2', group=gr2]
tab3 [label = '##3', group=gr3]
invis1 [style = invis, shape=point, width = 0, group=gr1]
invis1a [style = invis, shape=point, width = 0, group=gr2]
invis1b [style = invis, shape=point, width = 0, group=gr3]
# edge definitions with the node IDs
edge [arrowhead='none']
tab1 -> invis1;
invis1a -> invis1 -> invis1b; {rank=same invis1a invis1 invis1b}
edge [arrowhead='normal']
invis1a -> tab2;
invis1b -> tab3; {rank=same tab2 tab3}}
[1]: 'A'
[2]: 'B'
[3]: 'C'
")
This is my attempt to recreate the same graph with the nodes and edges solution:
# Packages needed for the test
library(DiagrammeR)
library(magrittr)
# Node and edge df solution
create_graph() %>%
add_node( # id 1
label = "A",
type = "group_1",
node_aes = node_aes(
style = "filled",
shape = "rectangle",
fixedsize = FALSE)
) %>%
add_node( # id 2
type = "group_1",
node_aes = node_aes(
style = "invisible",
height = 0,
width = 0)
) %>%
add_edge(
from = 1,
to = 2,
edge_aes = edge_aes(
arrowhead = "none")
) %>%
add_node( # id 3
type = "group_2",
node_aes = node_aes(
style = "invisible",
height = 0,
width = 0)
) %>%
add_edge(
from = 2,
to = 3,
edge_aes = edge_aes(
arrowhead = "none")
) %>%
add_node( # id 4
type = "group_3",
node_aes = node_aes(
style = "invisible",
height = 0,
width = 0)
) %>%
add_edge(
from = 2,
to = 4,
edge_aes = edge_aes(
arrowhead = "none")
) %>%
add_node( # id 5
label = "B",
type = "group_2",
node_aes = node_aes(
style = "filled",
shape = "rectangle",
fixedsize = FALSE)
) %>%
add_edge(
from = 3,
to = 5,
edge_aes = edge_aes(
arrowhead = "normal")
) %>%
add_node( # id 6
label = "C",
type = "group_3",
node_aes = node_aes(
style = "filled",
shape = "rectangle",
fixedsize = FALSE)
) %>%
add_edge(
from = 4,
to = 6,
edge_aes = edge_aes(
arrowhead = "normal")
) %>%
render_graph()
I have this two dataframes
1) t1 (Var1(numeric), Var2(string), Freq (numeric))
2) pref.media (pref.media (numeric)
I have this separated graphs, but I want to print like two series in the same graph
1) Column chart, ok
column1 <- highchart() %>%
hc_add_series(t1$Freq, type = "column") %>%
hc_xAxis(categories = t1$Var1) %>%
hc_plotOptions(series = list(showInLegend = FALSE,dataLabels = list(enabled = TRUE, color = t1$Var1)))
2) Solid gauge chart (ok)
gauge1 <- highchart(width = 800, height = 600) %>%
hc_chart(type = "solidgauge",backgroundColor = "#F0F0F0",marginTop = 50) %>%
hc_title(text = "Preferencia",style = list(fontSize = "24px")) %>%
hc_tooltip(borderWidth = 0, backgroundColor = 'none',shadow = FALSE,style = list(fontSize = '16px')) %>%
hc_pane(startAngle = -90,endAngle = 90) %>%
hc_yAxis(min = 0,max = 11,lineWidth = 0,tickPositions = list(0,1,2,3,4,5,6,7,8,9,10,11)) %>%
hc_plotOptions(solidgauge = list(borderWidth = '34px',dataLabels = list(enabled = TRUE, style = list(borderWidth = 3,backgroundColor = 'none',shadow = FALSE, fontSize = '16px',color="#888800")),linecap = 'round',stickyTracking = FALSE)) %>%
hc_add_series(name = "Preferencia",borderColor = "#000000",data = list(list(color = "#888800"),radius = "100%",innerRadius = "100%",y = round(pref.media[[1,1]],2)))
3) Mixing the charts (not ok, with errors)
highchart() %>%
hc_add_series(t1, "column", hcaes(x = Var1, y = Freq), name = "Preferencia") %>%
hc_add_series(pref.media, "solidgauge", hcaes(name = "Gauge", y = round(pref.media[[1,1]],2)), name = "Indicador") %>%
hc_plotOptions(
series = list(showInLegend = FALSE,pointFormat = "{point.y}%"),
column = list(colorByPoint = TRUE),
solidgauge = list(borderWidth = '34px',dataLabels = list(enabled = TRUE),linecap = 'round',stickyTracking = FALSE)) %>%
hc_pane(startAngle = -90,endAngle = 90) %>%
hc_yAxis(title = list(text = "Porcentaje de preferencia"),labels = list(format = "{value}%"), max = 100) %>%
hc_xAxis(categories = t1$Var1) %>%
hc_title(text = "Título") %>%
hc_subtitle(text = "Subtítulo")
I don't understand the sequence for mixing two series. I tried it with a J.Kunst's example in http://jkunst.com/highcharter/highcharts.html
Thanks