*This post continues from here
classifier = SVC()
parameters = {"kernel": ["rbf", "linear"],
"gamma": scipy.stats.expon(scale=.1),
"c": scipy.stats.expon(scale=100),
"class_weight": ["balanced", None]}
randomcv = RandomizedSearchCV(estimator=classifier, param_distributions=parameters,
scoring='accuracy', cv=10, n_jobs=-1,
random_state=0)
randomcv.fit(x_tu, y_tu)
Hi, I'm not sure what's causing the problem with the code above.
I'm getting a really long error message from here, and can't read what the error is telling me.
The same problem occurred when I was using a different classifier, but was able to solve them by changing the parameter range.
For this one, nothing seems to work for me.
The error says
Invalid parameter 'c' for SVC()
This is because the actual parameter is C (capital C), see SVC. Change the key c in the parameters to C and that should work
Related
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from engine import train_one_epoch, evaluate
import utils
import torchvision.transforms as T
num_epochs = 10
for epoch in range(num_epochs):
train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
lr_scheduler.step()
evaluate(model, data_loader_test, device=device)
I am using the same code as provided in this link Building Raccoon Model but mine is not working.
This is the error message I am getting
TypeError Traceback (most recent call last)
in ()
2 for epoch in range(num_epochs):
3 # train for one epoch, printing every 10 iterations
4 ----> train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)
5 # update the learning rate
6 lr_scheduler.step()
7 frames
in getitem(self, idx)
29 target["iscrowd"] = iscrowd
30 if self.transforms is not None:
31 ---> img, target = self.transforms(img, target)
32 return img, target
33
TypeError: call() takes 2 positional arguments but 3 were given
The above answer is incorrect, I accidentally upvoted before noticing. You are using the wrong Compose, note that it says
https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html#putting-everything-together
"In references/detection/, we have a number of helper functions to simplify training and evaluating detection models. Here, we will use references/detection/engine.py, references/detection/utils.py and references/detection/transforms.py. Just copy them to your folder and use them here."
there are helper scripts. They subclass the compose and flip methods
https://github.com/pytorch/vision/blob/6315358dd06e3a2bcbe9c1e8cdaa10898ac2b308/references/detection/transforms.py#L17
I did the same thing before noticing this. Do not use the compose method from torchvision.transforms, or else you will get the error above. Download their module and load it.
I am kind of a newbie at this and I was also having the same problem.
Upon doing more research, I found this where the accepted answer used:
img = self.transforms(img)
instead of:
img, target = self.transforms(img, target)
Removing "target" solved the error for me and should solve it for you as well. Not entirely sure why even the official PyTorch tutorial also has "target" included but it does not work for us.
I had the same issue, there is even an issue raised on Pytorch discussion forum using regarding the same T.Compose | TypeError: call() takes 2 positional arguments but 3 were given
I was able to overcome this issue by copy and pasting the files on the for a specific version v0.3.0 on the vision/reference/detection of the tutorial I am following building-your-own-object-detector-pytorch-vs-tensorflow-and-how-to-even-get-started
Just to fall into another issue I have raised here ValueError: All bounding boxes should have positive height and width. Found invaid box [500.728515625, 533.3333129882812, 231.10546875, 255.2083282470703] for target at index 0. #2740
I'm fairly new to machine learning and I am using the following code to encode my categorical data for preprocessing:
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([('one_hot_encoder', OneHotEncoder(handle_unknown = 'ignore'), [0])],remainder='passthrough')
X = np.array(ct.fit_transform(X), dtype=np.float)
which works when I only have one categorical column of data in X.
However when I have multiple columns of categorical data I change my code to :
ct = ColumnTransformer([('one_hot_encoder', OneHotEncoder(handle_unknown = 'ignore'), [0,1,2,3,4,5,10,14,15])],remainder='passthrough')
but I get the following error when calling the np.array function:
Value Error: setting an array element with a sequence
on the np.array function call...
From what I understand all I need to do is specify which columns I'm hot encoding as in the above line of code...so why does one work and the other give an error? What should I do to fix it?
Also: if I remove the
dtype=np.float
from the np.array function I don't get an error - but I also don't get anything returned in X
Never mind I was able to answer my own question.
For anyone interested what I did was change the line
X = np.array(ct.fit_transform(X), dtype=np.float)
to:
X = ct.fit_transform(X).toarray()
The code works perfectly now.
In case of 3 columns data, (In my test case) I can see that all the columns are valued as equal.
random_forest.feature_importances_
array([0.3131602 , 0.31915436, 0.36768544])
Is there any way to add waitage to one of the columns?
Update:
I guess xgboost can be used in this case.
I tried, but getting this error:
import xgboost as xgb
param = {}
num_round = 2
dtrain = xgb.DMatrix(X, y)
dtest = xgb.DMatrix(x_test_split)
dtrain_split = xgb.DMatrix(X_train, label=y_train)
dtest_split = xgb.DMatrix(X_test)
gbdt = xgb.train(param, dtrain_split, num_round)
y_predicted = gbdt.predict(dtest_split)
rmse_pred_vs_actual = xgb.rmse(y_predicted, y_test)
AttributeError: module 'xgboost' has no attribute 'rmse'
Error is by assuming xgb has method "rmse":
rmse_pred_vs_actual = xgb.rmse(y_predicted, y_test)
It is literally written: AttributeError: module 'xgboost' has no attribute 'rmse'
Use sklearn.metrics.mean_squared_error
By:
from sklearn.metrics import mean_squared_error
# Your code
rmse_pred_vs_actual = mean_squared_error(y_test, y_predicted)
It'll fix your error but it still doesn't control a feature importance.
Now, if you really want to change the importance of a feature, you need to be creative about how to make a change like this. There is no text book solution that I know of and no method in xgboost that I know of. You can follow the link Stev posted in a comment to your question and maybe get some ideas (including changing your ML algorithm).
I'm currently following this tutorial and after I did some changes because of the tensorflow update, I got this error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must be same size: logits_size=[399360,2] labels_size=[409920,2]
[[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Reshape_2, Reshape_3)]].
Can anyone help me with this one?
Changes in the code:
#Replaced concat_dim=2 with axis=2
combined_mask = tf.concat(axis=2, values=[bit_mask_class, bit_mask_background])
#Update the import of urllib2 to urllib3
#Replace tf.pack with tf.stack
upsampled_logits_shape = tf.stack([
downsampled_logits_shape[0],
downsampled_logits_shape[1] * upsample_factor,
downsampled_logits_shape[2] * upsample_factor,
downsampled_logits_shape[3]])
The error is raised because the number of logits is 399360 while you are providing to the function with 409920 labels. The function tf.nn.softmax_cross_entropy_with_logits expects one label for each logit, and it crashes because you are providing more labels than logits.
As to why it happens, you should post the changes you made to the code.
I am trying to run MultinomiaL Naive bayes and receiving the below error. Sample training data is given. Test data is exactly similar.
def main():
text_train, targets_train = read_data('train')
text_test, targets_test = read_data('test')
classifier1 = MultinomialNB()
classifier1.fit(text_train, targets_train)
prediction1 = classifier1.predict(text_test)
Sample Data:
Train:
category, text
Family, I love you Mom
University, I hate this course
Sometimes I face this question and find most of reason from the error is the input data should be 2-D array, such as if you want to build a regression model. you write this code and then you will face this error!
for example:
a = np.array([1,2,3]).T
b = np.array([4,5,6]).T
regr = linear_model.LinearRegression()
regr.fit(a, b)
then you should add something!
a = np.array([[1,2,3]]).T
b = np.array([[4,5,6]]).T
lastly you will be run normally!
so it is just my empirical!
This is just a reference, not a standard answer！
i am from Chinese as a student in learning English and python!