Please help me
I am not able plot using fancyRpart command though I have installed rattle and other dependency like RGtk2,rpart.plot& rpart.
I am using R version 3.4.2 (2017-09-28) on windows 10 getting following error
set.seed(123456)
modelFit<-train(classe ~.,method="rpart", data=TrainSet)
fancyrpartPlot(modelFit)
Error: the object passed to prp is not an rpart object
In addition: Warning message:
In max(model$frame$yval) : no non-missing arguments to max; returning -Inf
Please do provide complete reproducible exapmles otherwise we have to guess.
I think you are using caret::train(). This returns an object of class "train", not the actual final model but it does encapsulate the model and much more meta data: see ?caret::train.
Try:
fancyRpartPlot(modelFit$finalModel)
A reproducible example:
library(caret)
library(rattle)
modelFit<-train(Species ~., method="rpart", data=iris)
fancyRpartPlot(modelFit$finalModel)
Related
I'm running PyTorch Geometric (1.7.2) wrapped with PyTorch Lightning, and I get the assertion error above. I can't copy the whole stack trace, but the error is thrown by
return self.collate_fn(data)
I saw this article with the same error but it did not help. I would appreciate any tips on how to fix this.
I suppose that your data has some undesired attributes: 'batch' or 'ptr'. In order to check that, you can print 'data' directly.
Then to solve the error, you can easily turn off the 'batch' or 'ptr' attributes by:
for g in data:
g.batch = None
g.ptr = None
I've been trying to run the GluonCV tutorial for action recognition.
I didn't modify anything, but I'm getting an error at the very beginning of the script, when applying the transformation function to the image.
The error is:
AttributeError: 'list' object has no attribute 'shape'
To try and solve it, I wanted to replace the list with a single image, so I tried:
img = transform_fn(img.asnumpy())
plt.imshow(np.transpose(img, (1,2,0)))
plt.show()
but in this case, I get another error:
TypeError: Image data of dtype object cannot be converted to float
Any idea on how to fix it?
Thanks!
It seems I had to update gluoncv to a later version.
A little weird, because I installed it following the instruction on the tutorial page, but it works after the update.
I installed pyspc and run on Jupyter Notebook successfully when using original samples.
But when I tried introducing a self defined nested list and an error message showed up.
pyspc library: https://github.com/carlosqsilva/pyspc
from pyspc import*
import numpy
abc=[[2,3,4],[4,5.6],[1,4,5],[3,4,4],[4,5,6]]
a=spc(abc)+xbar_rbar()+rules()+rbar()
print(a)
error message for AssertionError
Thank you for advise where went wrong and how to fix it.
Check the data you have accidentally used the . instead of , for value [4,5.6], second element of the list.
Here is the corrected data
abc=[[2,3,4],[4,5,6],[1,4,5],[3,4,4],[4,5,6]]
Hope this will help.
While trying to run my Keras code on GPU (CUDA installed), I am not able to execute the following statement, as has been suggested on many online references.
set THEANO_FLAGS="mode=FAST_RUN,device=gpu,floatX=float32" & python theanogpu_example.py
I am getting the following error.
ValueError: Invalid value ("FAST_RUN,device=gpu,floatX=float32") for configurati
on variable "mode". Valid options are ('Mode', 'DebugMode', 'FAST_RUN', 'NanGuar
dMode', 'FAST_COMPILE', 'DEBUG_MODE')
I have tried the other mode suggested as well from inside the code.
import theano
theano.config.device = 'gpu'
theano.config.floatX = 'float32'
I get the following error.
Exception: Can't change the value of this config parameter after initialization!
Apart from knowing how to make it run, I would also take this opportunity to ask a simpler question. How to know in Windows what is my device i.e. whether 'gpu' or 'gpu1' or 'gpu0'? I have tried all 3 for my case but it hasn't yielded result.
Any suggestions will be appreciated.
The best way is using THEANO_FLAGS before run code, because the config variables cannot be changed after importing Theano, try this:
import os
os.environ['THEANO_FLAGS'] = "device=cuda,force_device=True,floatX=float32"
import theano
I have some experience with writing machine learning programs in python, but I'm new to TensorFlow and am checking it out. My dev environment is a lubuntu 14.04 64-bit virtual machine. I've created a python 3.5 conda environment from miniconda and installed TensorFlow 0.12 and its dependencies. I began trying to run some example code from TensorFlow's tutorials and encountered this warning when calling fit() in the boston.py example for input functions: source.
WARNING:tensorflow:Rank of input Tensor (1) should be the same as
output_rank (2) for column. Will attempt to expand dims. It is highly
recommended that you resize your input, as this behavior may change.
After some searching in Google, I found other people encountered this same warning:
https://github.com/tensorflow/tensorflow/issues/6184
https://github.com/tensorflow/tensorflow/issues/5098
Tensorflow - Boston Housing Data Tutorial Errors
However, they also experienced errors which prevent code execution from completing. In my case, the code executes with the above warning. Unfortunately, I couldn't find a single answer in those links regarding what caused the warning and how to fix the warning. They all focused on the error. How does one remove the warning? Or is the warning safe to ignore?
Cheers!
Extra info, I also see the following warnings when running the aforementioned boston.py example.
WARNING:tensorflow:*******************************************************
WARNING:tensorflow:TensorFlow's V1 checkpoint format has been
deprecated. WARNING:tensorflow:Consider switching to the more
efficient V2 format: WARNING:tensorflow:
'tf.train.Saver(write_version=tf.train.SaverDef.V2)'
WARNING:tensorflow:now on by default.
WARNING:tensorflow:*******************************************************
and
WARNING:tensorflow:From
/home/kade/miniconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py:1053
in predict.: calling BaseEstimator.predict (from
tensorflow.contrib.learn.python.learn.estimators.estimator) with x is
deprecated and will be removed after 2016-12-01. Instructions for
updating: Estimator is decoupled from Scikit Learn interface by moving
into separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion: est = Estimator(...) -> est =
SKCompat(Estimator(...))
UPDATE (2016-12-22):
I've tracked the warning to this file:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/feature_column_ops.py
and this code block:
except NotImplementedError:
with variable_scope.variable_scope(
None,
default_name=column.name,
values=columns_to_tensors.values()):
tensor = column._to_dense_tensor(transformed_tensor)
tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
variable = [
contrib_variables.model_variable(
name='weight',
shape=[tensor.get_shape()[1], num_outputs],
initializer=init_ops.zeros_initializer(),
trainable=trainable,
collections=weight_collections)
]
predictions = math_ops.matmul(tensor, variable[0], name='matmul')
Note the line: tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
The method signature is: _reshape_real_valued_tensor(input_tensor, output_rank, column_name=None)
The value 2 is hardcoded as the value of output_rank, but the boston.py example is passing in an input_tensor of rank 1. I will continue to investigate.
If you specify the shape of your tensor explicitly:
tf.constant(df[k].values, shape=[df[k].size, 1])
the warning should go away.
After I specify the shape of the tensor explicitly.
continuous_cols = {k: tf.constant(df[k].values, shape=[df[k].size, 1]) for k in CONTINUOUS_COLUMNS}
It works!