naive-bayes | Naive Bayes Text Classifier | Natural Language Processing library
kandi X-RAY | naive-bayes Summary
kandi X-RAY | naive-bayes Summary
Text classifier based on Naive Bayes.
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Top functions reviewed by kandi - BETA
- Train the model .
- Classify a set of documents .
- Extract the texts from a list of categories .
- Initialize the model .
- Returns the contents of a file
- Convert a category to a number .
naive-bayes Key Features
naive-bayes Examples and Code Snippets
Lines of Code : 2License : Strong Copyleft (CC BY-SA 4.0)
CopyX_TRAIN, X_IVS, y_TRAIN, y_IVS = train_test_split(x_d, y_d, test_size=0.10, random_state=23, stratify=y_d)
(df.groupby(['Name', 'System'])
['System'].agg(Cluster=','.join) # clusters of repeats
.droplevel('System').reset_index()
.groupby('Cluster')['Name'].agg(','.join) # aggregate by cluster
.reset_index()
)
# load both CountVectorizer and the model
vec = pickle.load(open("my_count_vec.pkl", "rb"))
sentiment_model = pickle.load(open("my_sentiment_model", "rb"))
@app.route('/journal', methods=['GET', 'POST'])
def entry():
if request.methdf.fillna(df.mean(), inplace=True)
eg: text= "abc"
>print(text[0]) #Output is 'a'.
>print(text['abc']) #Error - string indices must be integers
for index,row in df.iterrows():
text= row["Text"]
def classify(features_train, labels_train):
### import the sklearn module for GaussianNB
### create classifier
### fit the classifier on the training features and labels
### return the fit classifier
### yoevaluations_df.plot.bar(x='Model', y=['train_accuracy', 'test_accuracy'])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [Insert column number for your df])], remainder='passthrough')
X = np.array(ct.# Create a global dictionay
results = {}
for i in props:
size = int(i*len(X_train))
ix = np.random.choice(X_train.index, size=size, replace = False)
sampleX = X_train.loc[ix]
sampleY = y_train.loc[ix]
modelNB = Multinomimport random
# Split dataset into the k folds. Returns the list of k folds
def cross_validation_split(dataset, n_folds):
random.seed(0)
dataset_split = list()
dataset_copy = list(dataset)
fold_size = int(len(dataset) / n_Community Discussions
Trending Discussions on naive-bayes
QUESTION
I'm following a tutorial on Naive Bayes at https://towardsdatascience.com/why-how-to-use-the-naive-bayes-algorithms-in-a-regulated-industry-with-sklearn-python-code-dbd8304ab2cf but I'm stuck on interpreting the reference in the third code block to two_obs_test[continuous_list]
The full code listing is ...
...ANSWER
Answered 2021-Feb-11 at 19:52The tutorial has too many gaps. I think a view of the insides of Naive Bayes without reading a whole book is better found at https://machinelearningmastery.com/naive-bayes-classifier-scratch-python/ . I am not persisting with the tutorial and I advise others to avoid it.
QUESTION
For studying purposes, I've tried to implement this "lesson" using python but "without" sckitlearn or something similar.
My attempt code is the follow:
...ANSWER
Answered 2020-Nov-12 at 11:43You haven't multiplied by the priors p(Sport) = 3/5 and p(Not Sport) = 2/5. So just updating your answers by these ratios will get you to the correct result. Everything else looks good.
So for example you implement p(a|Sports) x p(very|Sports) x p(close|Sports) x p(game|Sports) in your math.prod(p) calculation but this ignores the term p(Sport). So adding this in (and doing the same for the not sport condition) fixes things.
In code this can be achieved by:
QUESTION
I'm doing a naive-bayes algorithm in R. The main goal is to predict a variable's value. But in this specific task, I'm trying to see which column is better at predicting it. This is an example of what works (but in the real dataset doing it manually isn't an option):
...ANSWER
Answered 2020-Jun-18 at 19:50This might be helpful. If you want to use a for loop, you can use seq_along with the names of your columns you want to loop through in your dataset. You can use reformulate to create a formula, which would you vsLog in your example, as well as the jth item in your column names. In this example, you can store your predict results in a list. Perhaps this might translate to your real dataset.
QUESTION
I am trying to understand how to create a dataframe of factors to predict an outcome using naive_bayes. All the examples I have seen take a single dataframe and split it into two dfs(training and test). This does work for me:
...ANSWER
Answered 2020-Jun-09 at 22:09For this particular case you probably can reference original levels by levels():
QUESTION
I am trying to classify multiclass data at the token-level using scikit-learn. I already have a train and test split. The tokens occurs in batches of the same class, e.g. first 10 tokens belonging to class0, the next 20 belonging to class4 and so on.
The data is in the following \t seperated format:
ANSWER
Answered 2020-May-29 at 08:19Instead of:
QUESTION
Alright, Im following https://medium.com/@phylypo/text-classification-with-scikit-learn-on-khmer-documents-1a395317d195 and https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html trying to classify text based on category. My dataframe is laid out like this and named result:
ANSWER
Answered 2020-May-10 at 08:05The mistake I believe is in these lines:
QUESTION
def loadCsv(filename):
lines = csv.reader(open('diabetes.csv'))
dataset = list(lines)
for i in range(len(dataset)):
dataset[i] = [float(x) for x in dataset[i]
return dataset
ANSWER
Answered 2020-Apr-01 at 13:45The ValueError is because the code is trying to cast (convert) the items in the CSV header row, which are strings, to floats. You could just skip the first row of the CSV file, for example:
QUESTION
I wanted to know if really AODE may be better than Naive Bayes in its way, as the description says:
https://cran.r-project.org/web/packages/AnDE/AnDE.pdf
--> "AODE achieves highly accurate classification by averaging over all of a small space."
https://www.quora.com/What-is-the-difference-between-a-Naive-Bayes-classifier-and-AODE
--> "AODE is a weird way of relaxing naive bayes' independence assumptions. It is no longer a generative model, but it relaxes the independence assumptions in a slightly different (and less principled) way than logistic regression does. It replaces the convex optimization problem used in training a logistic regression classifier by a quadratic (on the number of features) dependency on both training and test times."
But when I experiment it, I found that the predict results seems off, I implemented it with these codes:
...ANSWER
Answered 2020-Mar-12 at 13:00If you check out the vignette for the function:
train: data.frame : training data. It should be a data frame. AODE works only discretized data. It would be better to discreetize the data frame before passing it to this function.However, aode discretizes the data if not done before hand. It uses an R package called discretization for the purpose. It uses the well known MDL discretization technique.(It might fail sometimes)
By default, the discretization function from arules cuts it into 3, which may not be enough for iris. So I first reproduce the result you have with the discretization by arules:
QUESTION
I need to count probability of each word against each category. I tried this code, but the result not as my expected. It didn't show the if the count value is 0.
I have 2 table:
- tb_thesis --> id_thesis, title, topics
- tb_words --> id_word, id_thesis, word (this table contains tb_thesis which has been explode into single words)
ANSWER
Answered 2020-Feb-28 at 07:42use this query or understand the logic behind this
QUESTION
Not long ago I asked a question about the Accord.net Naive Bayes algorithm throwing an error. It turned out that this was due to me using Discrete value input columns but not giving enough training data for all the values I had listed for the column.
Now I am getting the exact same error, only this time it is being triggered only when I use a Continuous value for my output column. Particularly an output column of integer data type. Because it is an integer, the Codification class is not translating it so the values get passed directly into the Naive Bayes algorithm, and the algorithm apparently cannot handle that.
If I manually change the column data type to a string and send it through the Codification class to get codified then send the results of that through the algorithm it works correctly.
Is there any particular reason why this algorithm can't handle Continuous data types as outputs? Is there some setting I need to enable to make this work?
Some sample code:
...ANSWER
Answered 2019-Nov-13 at 17:06I don't have a great answer for this, however what I believe is occurring is that the algorithm I am using is listed on the accord.net site as a Classification algorithm.
Based on some reading here, my belief is that classification algorithms are not capable of handling continuous output values.
I probably need to switch to using a regression algorithm to gain that particular functionality.
In light of that, the solution for this algorithm is to manually codify the output column, or convert it to a string first so the Codification library will do the job for me.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install naive-bayes
You can use naive-bayes like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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