Problems classifiying labeled text, wrong prediction?

Problems classifiying labeled text, wrong prediction?

By : user2955393
Date : November 22 2020, 10:40 AM
I wish this help you I will leave it to you to get the training data into the expected format:
code :
training = ["this was a good movie",
            "this was a bad movie",
            "i went to the movies",
            "this movie was very exiting it was great", 
            "this is a boring film"]

labels = ['POS', 'NEG', 'NEU', 'POS', 'NEG']
>>> from sklearn.feature_extraction.text import HashingVectorizer
>>> vect = HashingVectorizer(n_features=5, stop_words='english', non_negative=True)
>>> X_train = vect.fit_transform(training)
>>> X_train.toarray()
[[ 0.          0.70710678  0.          0.          0.70710678]
 [ 0.70710678  0.70710678  0.          0.          0.        ]
 [ 0.          0.          0.          0.          0.        ]
 [ 0.          0.89442719  0.          0.4472136   0.        ]
 [ 1.          0.          0.          0.          0.        ]]
from sklearn.svm import SVC

model = SVC()
model.fit(X_train, labels)
>>> test = ["I don't like this movie it sucks it doesn't liked me"]
>>> X_pred = vect.transform(test)
>>> model.predict(X_pred)

>>> test = ["I think it was a good movie"]
>>> X_pred = vect.transform(test)
>>> model.predict(X_pred)

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Boost labeled graph, wrong internal map size

Boost labeled graph, wrong internal map size

By : lakhbir
Date : March 29 2020, 07:55 AM
Hope that helps I have created a labeled graph: , I found the line responsible for this:
code :
 * Choose the default map instance. If Label is an unsigned integral type
 * the we can use a vector to store the information.
template <typename Label, typename Vertex>
struct choose_default_map {
    typedef typename mpl::if_<
        std::map<Label, Vertex> // TODO: Should use unordered_map?
    >::type type;
ClearCase label problems: nobody except me can observe the files I labeled

ClearCase label problems: nobody except me can observe the files I labeled

By : user3116247
Date : March 29 2020, 07:55 AM
hope this fix your issue
Unfortunately its only me who can see the contents of vob with this my label
code :
element /vob_name/... label_name -mkbranch BRANCH_NAME 
element /vob_name/... /main/LATEST -mkbranch BRANCH_NAME 
Making sense of predicted probabilities for multi-labeled problems

Making sense of predicted probabilities for multi-labeled problems

By : Branflakes123
Date : March 29 2020, 07:55 AM
To fix this issue You can use .predict_proba(X_test) to get the probability per class on a new set of points. If you need a prediction for a single point, just make sure X_test has a single row.
The shape of the output should be in the shape [n_samples, n_classes] (in the test set). You can see the function's documentation.
Best practices for prediction of labeled data (numpy)

Best practices for prediction of labeled data (numpy)

By : D. Ankrah
Date : March 29 2020, 07:55 AM
will help you Indeed you need to split your data. You even need to split it in 3 for validation purposes.
Regarding the random split, you need to make sure that labels and data stay aligned or you will learn nothing but randomness from your data. For instance (pythonic pseudo code since you did not provide any code...)
code :
from random import sample

indices = sample(xrange(N), M) # generate M non repeating indices between 0 and N
remaining_indices = list(set(xrange(N)) - set(indices)) # Use sets to easily get the indices you left behind
train_set = data_set[indices]
train_labels = labels[indices]
test_set =  data_set[remaining_indices]
test_labels = labels[remaining_indices]
Text generation: character prediction RNN vs. word prediction RNN

Text generation: character prediction RNN vs. word prediction RNN

By : Henrique Guedes
Date : March 29 2020, 07:55 AM
hop of those help?
Why wouldn't you do the same technique but using words instead of characters.
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