I have created a Gaussian Naive Bayes classifier on a email (spam/not spam) dataset and was able to run it successfully. I vectorized the data, divided in it train and test sets and then calculated the accuracy, all the features that are present in the sklearn-Gaussian Naive Bayes classifier.
Now I want to be able to use this classifier to predict "labels" for new emails - whether they are by spam or not.
For example say I have an email. I want to feed it to my classifier and get the prediction as to whether it is a spam or not. How can I achieve this? Please Help.
Code for classifier file.
#!/usr/bin/python
import sys
from time import time
import logging
# Display progress logs on stdout
logging.basicConfig(level = logging.DEBUG, format = '%(asctime)s %(message)s')
sys.path.append("../DatasetProcessing/")
from vectorize_split_dataset import preprocess
### features_train and features_test are the features
for the training and testing datasets, respectively### labels_train and labels_test are the corresponding item labels
features_train, features_test, labels_train, labels_test = preprocess()
#########################################################
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
t0 = time()
clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
print("training time:", round(time() - t0, 3), "s")
print(clf.score(features_test, labels_test))
## Printing Metrics
for Training and Testing
print("No. of Testing Features:" + str(len(features_test)))
print("No. of Testing Features Label:" + str(len(labels_test)))
print("No. of Training Features:" + str(len(features_train)))
print("No. of Training Features Label:" + str(len(labels_train)))
print("No. of Predicted Features:" + str(len(pred)))
## Calculating Classifier Performance
from sklearn.metrics import classification_report
y_true = labels_test
y_pred = pred
labels = ['0', '1']
target_names = ['class 0', 'class 1']
print(classification_report(y_true, y_pred, target_names = target_names, labels = labels))
# How to predict label of a new text
new_text = "You won a lottery at UK lottery commission. Reply to claim it"
Code for Vectorization
#!/usr/bin/python
import os
import pickle
import numpy
numpy.random.seed(42)
path = os.path.dirname(os.path.abspath(__file__))
### The words(features) and label_data(labels), already largely processed.###These files should have been created beforehand
feature_data_file = path + "./createdDataset/dataSet.pkl"
label_data_file = path + "./createdDataset/dataLabel.pkl"
feature_data = pickle.load(open(feature_data_file, "rb"))
label_data = pickle.load(open(label_data_file, "rb"))
### test_size is the percentage of events assigned to the test set(the### remainder go into training)### feature matrices changed to dense representations
for compatibility with### classifier functions in versions 0.15.2 and earlier
from sklearn import cross_validation
features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(feature_data, label_data, test_size = 0.1, random_state = 42)
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(sublinear_tf = True, max_df = 0.5, stop_words = 'english')
features_train = vectorizer.fit_transform(features_train)
features_test = vectorizer.transform(features_test)#.toarray()
## feature selection to reduce dimensionality
from sklearn.feature_selection import SelectPercentile, f_classif
selector = SelectPercentile(f_classif, percentile = 5)
selector.fit(features_train, labels_train)
features_train_transformed_reduced = selector.transform(features_train).toarray()
features_test_transformed_reduced = selector.transform(features_test).toarray()
features_train = features_train_transformed_reduced
features_test = features_test_transformed_reduced
def preprocess():
return features_train, features_test, labels_train, labels_test
Code for dataset generation
#!/usr/bin/python
import os
import pickle
import re
import sys
# sys.path.append("../tools/")
""
"
Starter code to process the texts of accuate and inaccurate category to extract
the features and get the documents ready for classification.
The list of all the texts from accurate category are in the accurate_files list
likewise for texts of inaccurate category are in (inaccurate_files)
The data is stored in lists and packed away in pickle files at the end.
"
""
accurate_files = open("./rawDatasetLocation/accurateFiles.txt", "r")
inaccurate_files = open("./rawDatasetLocation/inaccurateFiles.txt", "r")
label_data = []
feature_data = []
### temp_counter is a way to speed up the development--there are### thousands of lines of accurate and inaccurate text, so running over all of them### can take a long time### temp_counter helps you only look at the first 200 lines in the list so you### can iterate your modifications quicker
temp_counter = 0
for name, from_text in [("accurate", accurate_files), ("inaccurate", inaccurate_files)]:
for path in from_text: ###only look at first 200 texts when developing### once everything is working, remove this line to run over full dataset
temp_counter = 1
if temp_counter < 200:
path = os.path.join('..', path[: -1])
print(path)
text = open(path, "r")
line = text.readline()
while line: ###use a
function parseOutText to extract the text from the opened text# stem_text = parseOutText(text)
stem_text = text.readline().strip()
print(stem_text)### use str.replace() to remove any instances of the words# stem_text = stem_text.replace("germani", "")### append the text to feature_data
feature_data.append(stem_text)### append a 0 to label_data
if text is from Sara, and 1
if text is from Chris
if (name == "accurate"):
label_data.append("0")
elif(name == "inaccurate"):
label_data.append("1")
line = text.readline()
text.close()
print("texts processed")
accurate_files.close()
inaccurate_files.close()
pickle.dump(feature_data, open("./createdDataset/dataSet.pkl", "wb"))
pickle.dump(label_data, open("./createdDataset/dataLabel.pkl", "wb"))
Also I want to know whether i can incrementally train the classifier meaning thereby that retrain a created model with newer data for refining the model over time?
I would be really glad if someone can help me out with this. I am really stuck at this point.
You are already using your model to predict labels of emails in your test set. This is what pred = clf.predict(features_test) does. If you want to see these labels, do print pred.
But perhaps you what to know how you can predict labels for emails that you discover in the future and that are not currently in your test set? If so, you can think of your new email(s) as a new test set. As with your previous test set, you will need to run several key processing steps on the data:
1) The first thing you need to do is to generate features for your new email data. The feature generation step is not included in your code above, but will need to occur.
2) You are using a Tfidf vectorizer, which converts a collection of documents to a matrix of Tfidf features based upon term frequency and inverse document frequency. You need to put your new email test feature data through the vectorizer that you fit on your training data.
3) Then your new email test feature data will need to go through dimensionality reduction using the same selector that you fit on your training data.
4) Finally, run predict on your new test data. Use print pred if you want to view the new label(s).
To respond to your final question about iteratively re-training your model, yes you definitely can do this. It's just a matter of selecting a frequency, producing a script that expands your data set with incoming data, then re-running all steps from there, from pre-processing to Tfidf vectorization, to dimensionality reduction, to fitting, and prediction.
Related
I am really struggling to make things work with the new spacy v3 version. The documentation is full. However, I am trying to run a training loop in a script.
(I am also not able to perform text classification training with CLI approach).
Data are publically available here.
import pandas as pd
from spacy.training import Example
import random
TRAIN_DATA = pd.read_json('data.jsonl', lines = True)
nlp = spacy.load('en_core_web_sm')
config = {
"threshold": 0.5,
}
textcat = nlp.add_pipe("textcat", config=config, last=True)
label = TRAIN_DATA['label'].unique()
for label in label:
textcat.add_label(str(label))
nlp = spacy.blank("en")
nlp.begin_training()
# Loop for 10 iterations
for itn in range(100):
# Shuffle the training data
losses = {}
TRAIN_DATA = TRAIN_DATA.sample(frac = 1)
# Batch the examples and iterate over them
for batch in spacy.util.minibatch(TRAIN_DATA.values, size=4):
texts = [nlp.make_doc(text) for text, entities in batch]
annotations = [{"cats": entities} for text, entities in batch]
# uses an example object rather than text/annotation tuple
print(texts)
print(annotations)
examples = [Example.from_dict(a)]
nlp.update(examples, losses=losses)
if itn % 20 == 0:
print(losses)
In Pytorch, is there any way of loading a specific single sample using the torch.utils.data.DataLoader class? I'd like to do some testing with it.
The tutorial uses
trainloader = torch.utils.data.DataLoader(...)
images, labels = next(iter(trainloader))
to fetch a random batch of samples. Is there are way, using DataLoader, to get a specific sample?
Cheers
Turn off the shuffle in DataLoader
Use batch_size to calculate the batch in which the desired sample you are looking for falls in
Iterate to the desired batch
Code
import torch
import numpy as np
import itertools
X= np.arange(100)
batch_size = 2
dataloader = torch.utils.data.DataLoader(X, batch_size=batch_size, shuffle=False)
sample_at = 5
k = int(np.floor(sample_at/batch_size))
my_sample = next(itertools.islice(dataloader, k, None))
print (my_sample)
Output:
tensor([4, 5])
if you want to get a specific signle sample from your dataset you can
you should check Subset class.(https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset)
something like this:
indices = [0,1,2] # select your indices here as a list
subset = torch.utils.data.Subset(train_set, indices)
trainloader = DataLoader(subset , batch_size = 16 , shuffle =False) #set shuffle to False
for image , label in trainloader:
print(image.size() , '\t' , label.size())
print(image[0], '\t' , label[0]) # index the specific sample
here is a useful link if you want to learn more about the Pytorch data loading utility
(https://pytorch.org/docs/stable/data.html)
I want to retrain pre-trained word embeddings in Python using Gensim. The pre-trained embeddings I want to use is Google's Word2Vec in the file GoogleNews-vectors-negative300.bin.
Following Gensim's word2vec tutorial, "it’s not possible to resume training with models generated by the C tool, load_word2vec_format(). You can still use them for querying/similarity, but information vital for training (the vocab tree) is missing there."
Therefore I can't use the KeyedVectors and for training a model the tutorial suggests to use:
model = gensim.models.Word2Vec.load('/tmp/mymodel')
model.train(more_sentences)
(https://rare-technologies.com/word2vec-tutorial/)
However, when I try this:
from gensim.models import Word2Vec
model = Word2Vec.load('data/GoogleNews-vectors-negative300.bin')
I get an error message:
1330 # Because of loading from S3 load can't be used (missing readline in smart_open)
1331 if sys.version_info > (3, 0):
-> 1332 return _pickle.load(f, encoding='latin1')
1333 else:
1334 return _pickle.loads(f.read())
UnpicklingError: invalid load key, '3'.
I didn't find a way to convert the binary google new file into a text file properly, and even if so I'm not sure whether that would solve my problem.
Does anyone have a solution to this problem or knows about a different way to retrain pre-trained word embeddings?
The Word2Vec.load() method can only load full models in gensim's native format (based on Python object-pickling) – not any other binary/text formats.
And, as per the documentation's note that "it’s not possible to resume training with models generated by the C tool", there's simply not enough information in the GoogleNews raw-vectors files to reconstruct the full working model that was used to train them. (That would require both some internal model-weights, not saved in that file, and word-frequency-information for controlling sampling, also not saved in that file.)
The best you could do is create a new Word2Vec model, then patch some/all of the GoogleNews vectors into it before doing your own training. This is an error-prone process with no real best-practices and many caveats about the interpretation of final results. (For example, if you bring in all the vectors, but then only re-train a subset using only your own corpus & word-frequencies, the more training you do – making the word-vectors better fit your corpus – the less such re-trained words will have any useful comparability to retained untrained words.)
Essentially, if you can look at the gensim Word2Vec source & work-out how to patch-together such a frankenstein-model, it may be appropriate. But there's no built-in support or handy off-the-shelf recipes that make it easy, because it's an inherently murky process.
I have already answered it here .
Save the google news model as text file in wor2vec format using gensim.
Refer this answer to save it as text file
Then try this code .
import os
import pickle
import numpy as np
import gensim
from gensim.models import Word2Vec, KeyedVectors
from gensim.models.callbacks import CallbackAny2Vec
import operator
os.mkdir("model_dir")
# class EpochSaver(CallbackAny2Vec):
# '''Callback to save model after each epoch.'''
# def __init__(self, path_prefix):
# self.path_prefix = path_prefix
# self.epoch = 0
# def on_epoch_end(self, model):
# list_of_existing_files = os.listdir(".")
# output_path = 'model_dir/{}_epoch{}.model'.format(self.path_prefix, self.epoch)
# try:
# model.save(output_path)
# except:
# model.wv.save_word2vec_format('model_dir/model_{}.bin'.format(self.epoch), binary=True)
# print("number of epochs completed = {}".format(self.epoch))
# self.epoch += 1
# list_of_total_files = os.listdir(".")
# saver = EpochSaver("my_finetuned")
# function to load vectors from existing model.
# I am loading glove vectors from a text file, benefit of doing this is that I get complete vocab of glove as well.
# If you are using a previous word2vec model I would recommed save that in txt format.
# In case you decide not to do it, you can tweak the function to get vectors for words in your vocab only.
def load_vectors(token2id, path, limit=None):
embed_shape = (len(token2id), 300)
freqs = np.zeros((len(token2id)), dtype='f')
vectors = np.zeros(embed_shape, dtype='f')
i = 0
with open(path, encoding="utf8", errors='ignore') as f:
for o in f:
token, *vector = o.split(' ')
token = str.lower(token)
if len(o) <= 100:
continue
if limit is not None and i > limit:
break
vectors[token2id[token]] = np.array(vector, 'f')
i += 1
return vectors
# path of text file of your word vectors.
embedding_name = "word2vec.txt"
data = "<training data(new line separated tect file)>"
# Dictionary to store a unique id for each token in vocab( in my case vocab contains both my vocab and glove vocab)
token2id = {}
# This dictionary will contain all the words and their frequencies.
vocab_freq_dict = {}
# Populating vocab_freq_dict and token2id from my data.
id_ = 0
training_examples = []
file = open("{}".format(data),'r', encoding="utf-8")
for line in file.readlines():
words = line.strip().split(" ")
training_examples.append(words)
for word in words:
if word not in vocab_freq_dict:
vocab_freq_dict.update({word:0})
vocab_freq_dict[word] += 1
if word not in token2id:
token2id.update({word:id_})
id_ += 1
# Populating vocab_freq_dict and token2id from glove vocab.
max_id = max(token2id.items(), key=operator.itemgetter(1))[0]
max_token_id = token2id[max_id]
with open(embedding_name, encoding="utf8", errors='ignore') as f:
for o in f:
token, *vector = o.split(' ')
token = str.lower(token)
if len(o) <= 100:
continue
if token not in token2id:
max_token_id += 1
token2id.update({token:max_token_id})
vocab_freq_dict.update({token:1})
with open("vocab_freq_dict","wb") as vocab_file:
pickle.dump(vocab_freq_dict, vocab_file)
with open("token2id", "wb") as token2id_file:
pickle.dump(token2id, token2id_file)
# converting vectors to keyedvectors format for gensim
vectors = load_vectors(token2id, embedding_name)
vec = KeyedVectors(300)
vec.add(list(token2id.keys()), vectors, replace=True)
# setting vectors(numpy_array) to None to release memory
vectors = None
params = dict(min_count=1,workers=14,iter=6,size=300)
model = Word2Vec(**params)
# using build from vocab to build the vocab
model.build_vocab_from_freq(vocab_freq_dict)
# using token2id to create idxmap
idxmap = np.array([token2id[w] for w in model.wv.index2entity])
# Setting hidden weights(syn0 = between input layer and hidden layer) = your vectors arranged accoring to ids
model.wv.vectors[:] = vec.vectors[idxmap]
# Setting hidden weights(syn0 = between hidden layer and output layer) = your vectors arranged accoring to ids
model.trainables.syn1neg[:] = vec.vectors[idxmap]
model.train(training_examples, total_examples=len(training_examples), epochs=model.epochs)
output_path = 'model_dir/final_model.model'
model.save(output_path)
I am using a keras neural net for identifying category in which the data belongs.
self.model.compile(loss='categorical_crossentropy',
optimizer=keras.optimizers.Adam(lr=0.001, decay=0.0001),
metrics=[categorical_accuracy])
Fit function
history = self.model.fit(self.X,
{'output': self.Y},
validation_split=0.3,
epochs=400,
batch_size=32
)
I am interested in finding out which labels are getting categorized wrongly in the validation step. Seems like a good way to understand what is happening under the hood.
You can use model.predict_classes(validation_data) to get the predicted classes for your validation data, and compare these predictions with the actual labels to find out where the model was wrong. Something like this:
predictions = model.predict_classes(validation_data)
wrong = np.where(predictions != Y_validation)
If you are interested in looking 'under the hood', I'd suggest to use
model.predict(validation_data_x)
to see the scores for each class, for each observation of the validation set.
This should shed some light on which categories the model is not so good at classifying. The way to predict the final class is
scores = model.predict(validation_data_x)
preds = np.argmax(scores, axis=1)
be sure to use the proper axis for np.argmax (I'm assuming your observation axis is 1). Use preds to then compare with the real class.
Also, as another exploration you want to see the overall accuracy on this dataset, use
model.evaluate(x=validation_data_x, y=validation_data_y)
I ended up creating a metric which prints the "worst performing category id + score" on each iteration. Ideas from link
import tensorflow as tf
import numpy as np
class MaxIoU(object):
def __init__(self, num_classes):
super().__init__()
self.num_classes = num_classes
def max_iou(self, y_true, y_pred):
# Wraps np_max_iou method and uses it as a TensorFlow op.
# Takes numpy arrays as its arguments and returns numpy arrays as
# its outputs.
return tf.py_func(self.np_max_iou, [y_true, y_pred], tf.float32)
def np_max_iou(self, y_true, y_pred):
# Compute the confusion matrix to get the number of true positives,
# false positives, and false negatives
# Convert predictions and target from categorical to integer format
target = np.argmax(y_true, axis=-1).ravel()
predicted = np.argmax(y_pred, axis=-1).ravel()
# Trick from torchnet for bincounting 2 arrays together
# https://github.com/pytorch/tnt/blob/master/torchnet/meter/confusionmeter.py
x = predicted + self.num_classes * target
bincount_2d = np.bincount(x.astype(np.int32), minlength=self.num_classes**2)
assert bincount_2d.size == self.num_classes**2
conf = bincount_2d.reshape((self.num_classes, self.num_classes))
# Compute the IoU and mean IoU from the confusion matrix
true_positive = np.diag(conf)
false_positive = np.sum(conf, 0) - true_positive
false_negative = np.sum(conf, 1) - true_positive
# Just in case we get a division by 0, ignore/hide the error and set the value to 0
with np.errstate(divide='ignore', invalid='ignore'):
iou = false_positive / (true_positive + false_positive + false_negative)
iou[np.isnan(iou)] = 0
return np.max(iou).astype(np.float32) + np.argmax(iou).astype(np.float32)
~
usage:
custom_metric = MaxIoU(len(catagories))
self.model.compile(loss='categorical_crossentropy',
optimizer=keras.optimizers.Adam(lr=0.001, decay=0.0001),
metrics=[categorical_accuracy, custom_metric.max_iou])
I want to perform text classification using word2vec.
I got vectors of words.
ls = []
sentences = lines.split(".")
for i in sentences:
ls.append(i.split())
model = Word2Vec(ls, min_count=1, size = 4)
words = list(model.wv.vocab)
print(words)
vectors = []
for word in words:
vectors.append(model[word].tolist())
data = np.array(vectors)
data
output:
array([[ 0.00933912, 0.07960335, -0.04559333, 0.10600036],
[ 0.10576613, 0.07267512, -0.10718666, -0.00804013],
[ 0.09459028, -0.09901826, -0.07074171, -0.12022413],
[-0.09893986, 0.01500741, -0.04796079, -0.04447284],
[ 0.04403428, -0.07966098, -0.06460238, -0.07369237],
[ 0.09352681, -0.03864434, -0.01743148, 0.11251986],.....])
How can i perform classification (product & non product)?
You already have the array of word vectors using model.wv.syn0. If you print it, you can see an array with each corresponding vector of a word.
You can see an example here using Python3:
import pandas as pd
import os
import gensim
import nltk as nl
from sklearn.linear_model import LogisticRegression
#Reading a csv file with text data
dbFilepandas = pd.read_csv('machine learning\\Python\\dbSubset.csv').apply(lambda x: x.astype(str).str.lower())
train = []
#getting only the first 4 columns of the file
for sentences in dbFilepandas[dbFilepandas.columns[0:4]].values:
train.extend(sentences)
# Create an array of tokens using nltk
tokens = [nl.word_tokenize(sentences) for sentences in train]
Now it's time to use the vector model, in this example we will calculate the LogisticRegression.
# method 1 - using tokens in Word2Vec class itself so you don't need to train again with train method
model = gensim.models.Word2Vec(tokens, size=300, min_count=1, workers=4)
# method 2 - creating an object 'model' of Word2Vec and building vocabulary for training our model
model = gensim.models.Word2vec(size=300, min_count=1, workers=4)
# building vocabulary for training
model.build_vocab(tokens)
print("\n Training the word2vec model...\n")
# reducing the epochs will decrease the computation time
model.train(tokens, total_examples=len(tokens), epochs=4000)
# You can save your model if you want....
# The two datasets must be the same size
max_dataset_size = len(model.wv.syn0)
Y_dataset = []
# get the last number of each file. In this case is the department number
# this will be the 0 or 1, or another kind of classification. ( to use words you need to extract them differently, this way is to numbers)
with open("dbSubset.csv", "r") as f:
for line in f:
lastchar = line.strip()[-1]
if lastchar.isdigit():
result = int(lastchar)
Y_dataset.append(result)
else:
result = 40
clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial').fit(model.wv.syn0, Y_dataset[:max_dataset_size])
# Prediction of the first 15 samples of all features
predict = clf.predict(model.wv.syn0[:15, :])
# Calculating the score of the predictions
score = clf.score(model.wv.syn0, Y_dataset[:max_dataset_size])
print("\nPrediction word2vec : \n", predict)
print("Score word2vec : \n", score)
You can also calculate the similarity of words belonging to your created model dictionary:
print("\n\nSimilarity value : ",model.wv.similarity('women','men'))
You can find more functions to use here.
Your question is rather broad but I will try to give you a first approach to classify text documents.
First of all, I would decide how I want to represent each document as one vector. So you need a method that takes a list of vectors (of words) and returns one single vector. You want to avoid that the length of the document influences what this vector represents. You could for example choose the mean.
def document_vector(array_of_word_vectors):
return array_of_word_vectors.mean(axis=0)
where array_of_word_vectors is for example data in your code.
Now you can either play a bit around with distances (for example cosine distance would a nice first choice) and see how far certain documents are from each other or - and that's probably the approach that brings faster results - you can use the document vectors to build a training set for a classification algorithm of your choice from scikit learn, for example Logistic Regression.
The document vectors will become your matrix X and your vector y is an array of 1 and 0, depending on the binary category that you want the documents to be classified into.