I'm using gensim's LdaModel, which, according to the documentation, has the parameter random_state. However, I'm getting an error that says:
TypeError: __init__() got an unexpected keyword argument 'random_state'
Without the random_state parameter, the function works as expected. So, the workflow looks like this for those that want to know what else is happening...
from gensim import corpora, models
import numpy as np
# pseudo code of text pre-processing all on "comments" variable
# stop words
# remove punctuation (optional)
# keep alpha only
# stemming
# get bigrams and integrate with corpus (gensim makes this very easy)
dictionary = corpora.Dictionary(comments)
corpus = [dictionary.doc2bow(comm) for comm in comments]
tfidf = models.TfidfModel(corpus) # change weights
corp_tfidf = tfidf[corpus] # apply them to corpus
# set random seed
random_seed = 135
state = np.random.RandomState(random_seed)
# train model
num_topics = 3
lda_mod = models.LdaModel(corp_tfidf, # corpus
num_topics=num_topics, # number of topics we want back
id2word=dictionary, # our id-word map
passes=10, # how many passes to take over the data
random_state=state) # reproduce the results
Which results in the error message above...
TypeError: __init__() got an unexpected keyword argument 'random_state'
I'd like to be able to recreate my results, if possible.
According to this, random_state parameter was added in the latest version (0.13.2). You can update your gensim installation with pip install gensim --upgrade. You might need to update scipy first, because it caused me problems.
Related
I'm quite new to TFX (TensorFlow Extended), and have been going through the sample tutorial on the TensorFlow portal to understand a bit more to apply it to my dataset.
In my scenario, instead of predicting a single label, the problem at hand requires me to predict 2 outputs (category 1, category 2).
I've done this using pure TensorFlow Keras Functional API and that works fine, but then am now looking to see if that can be fitted into the TFX pipeline.
Where i get the error, is at the Trainer stage of the pipeline, and where it throws the error is in the _input_fn, and i suspect it's because i'm not correctly splitting out the given data into (features, labels) tensor pair in the pipeline.
Scenario:
Each row of the input data comes in the form of
[Col1, Col2, Col3, ClassificationA, ClassificationB]
ClassificationA and ClassificationB are the categorical labels which i'm trying to predict using the Keras Functional Model
The output layer of the keras functional model looks like below, where there's 2 outputs that is joined to a single dense layer (Note: _xf appended to the end is just to illustrate that i've encoded the classes to int representations)
output_1 = tf.keras.layers.Dense(
TargetA_Class, activation='sigmoid',
name = 'ClassificationA_xf')(dense)
output_2 = tf.keras.layers.Dense(
TargetB_Class, activation='sigmoid',
name = 'ClassificationB_xf')(dense)
model = tf.keras.Model(inputs = inputs,
outputs = [output_1, output_2])
In the trainer module file, i've imported the required packages at the start of the module file >
import tensorflow_transform as tft
from tfx.components.tuner.component import TunerFnResult
import tensorflow as tf
from typing import List, Text
from tfx.components.trainer.executor import TrainerFnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor, FnArgs
from tfx_bsl.tfxio import dataset_options
The current input_fn in the trainer module file looks like the below (by following the tutorial)
def _input_fn(file_pattern: List[Text],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
"""Helper function that Generates features and label dataset for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
return data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size,
#label_key=[_transformed_name(x) for x in _CATEGORICAL_LABEL_KEYS]),
label_key=_transformed_name(_CATEGORICAL_LABEL_KEYS[0]), _transformed_name(_CATEGORICAL_LABEL_KEYS[1])),
tf_transform_output.transformed_metadata.schema)
When i run the trainer component the error that comes up is:
label_key=_transformed_name(_CATEGORICAL_LABEL_KEYS[0]),transformed_name(_CATEGORICAL_LABEL_KEYS1)),
^ SyntaxError: positional argument follows keyword argument
I've also tried label_key=[_transformed_name(x) for x in _CATEGORICAL_LABEL_KEYS]) which also gives an error.
However, if i just pass in a single label key, label_key=transformed_name(_CATEGORICAL_LABEL_KEYS[0]) then it works fine.
FYI - _CATEGORICAL_LABEL_KEYS is nothing but a list which contains the names of the 2 outputs i'm trying to predict (ClassificationA, ClassificationB).
transformed_name is nothing but a function to return an updated name/key for the transformed data:
def transformed_name(key):
return key + '_xf'
Question:
From what i can see, the label_key argument for dataset_options.TensorFlowDatasetOptions can only accept a single string/name of label, which means it may not be able to output the dataset with multi labels.
Is there a way which i can modify the _input_fn so that i can get the dataset that's returned by _input_fn to work with returning the 2 output labels? So the tensor that's returned looks something like:
Feature_Tensor: {Col1_xf: Col1_transformedfeature_values, Col2_xf:
Col2_transformedfeature_values, Col3_xf:
Col3_transformedfeature_values}
Label_Tensor: {ClassificationA_xf: ClassA_encodedlabels,
ClassificationB_xf: ClassB_encodedlabels}
Would appreciate advice from the wider community of tfx!
Since the label key is optional, maybe instead of specifying it in the TensorflowDatasetOptions, instead you can use dataset.map afterwards and pass both labels after taking them from your dataset.
Haven't tested it but something like:
def _data_augmentation(feature_dict):
features = feature_dict[_transformed_name(x) for x in
_CATEGORICAL_FEATURE_KEYS]]
keys=[_transformed_name(x) for x in _CATEGORICAL_LABEL_KEYS]
return features, keys
def _input_fn(file_pattern: List[Text],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
batch_size: int = 200) -> tf.data.Dataset:
"""Helper function that Generates features and label dataset for tuning/training.
Args:
file_pattern: List of paths or patterns of input tfrecord files.
data_accessor: DataAccessor for converting input to RecordBatch.
tf_transform_output: A TFTransformOutput.
batch_size: representing the number of consecutive elements of returned
dataset to combine in a single batch
Returns:
A dataset that contains (features, indices) tuple where features is a
dictionary of Tensors, and indices is a single Tensor of label indices.
"""
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size,
tf_transform_output.transformed_metadata.schema)
dataset = dataset.map(_data_augmentation)
return dataset
I am trying to use a pretrained word2vector model to create word embeddings but i am getting the following error when Im trying to create weight matrix from word2vec genism model:
Code:
import gensim
w2v_model = gensim.models.KeyedVectors.load_word2vec_format("/content/drive/My Drive/GoogleNews-vectors-negative300.bin.gz", binary=True)
vocab_size = len(tokenizer.word_index) + 1
print(vocab_size)
EMBEDDING_DIM=300
# Function to create weight matrix from word2vec gensim model
def get_weight_matrix(model, vocab):
# total vocabulary size plus 0 for unknown words
vocab_size = len(vocab) + 1
# define weight matrix dimensions with all 0
weight_matrix = np.zeros((vocab_size, EMBEDDING_DIM))
# step vocab, store vectors using the Tokenizer's integer mapping
for word, i in vocab.items():
weight_matrix[i] = model[word]
return weight_matrix
embedding_vectors = get_weight_matrix(w2v_model, tokenizer.word_index)
Im getting the following error:
Error
As a note: it's better to paste a full error is as formatted text than as an image of text. (See Why not upload images of code/errors when asking a question? for a full list of the reasons why.)
But regarding your question:
If you get a KeyError: word 'didnt' not in vocabulary error, you can trust that the word you've requested is not in the set-of-word-vectors you've requested it from. (In this case, the GoogleNews vectors that Google trained & released back around 2012.)
You could check before looking it up – 'didnt' in w2v_model, which would return False, and then do something else. Or you could use a Python try: ... catch: ... formulation to let it happen, but then do something else when it happens.
But it's up to you what your code should do if the model you've provided doesn't have the word-vectors you were hoping for.
(Note: the GoogleNews vectors do include a vector for "didn't", the contraction with its internal apostrophe. So in this one case, the issue may be that your tokenization is stripping such internal-punctuation-marks from contractions, but Google chose not to when making those vectors. But your code should be ready for handling missing words in any case, unless you're sure through other steps that can never happen.)
Just curiosity, but I was debugging gensim's FastText code for replicating the implementation of Out-of-Vocabulary (OOV) words, and I'm not being able to accomplish it.
So, the process i'm following is training a tiny model with a toy corpus, and then comparing the resulting vectors of a word in the vocabulary. That means if the whole process is OK, the output arrays should be the same.
Here is the code I've used for the test:
from gensim.models import FastText
import numpy as np
# Default gensim's function for hashing ngrams
from gensim.models._utils_any2vec import ft_hash_bytes
# Toy corpus
sentences = [['hello', 'test', 'hello', 'greeting'],
['hey', 'hello', 'another', 'test']]
# Instatiate FastText gensim's class
ft = FastText(sg=1, size=5, min_count=1, \
window=2, hs=0, negative=20, \
seed=0, workers=1, bucket=100, \
min_n=3, max_n=4)
# Build vocab
ft.build_vocab(sentences)
# Fit model weights (vectors_ngram)
ft.train(sentences=sentences, total_examples=ft.corpus_count, epochs=5)
# Save model
ft.save('./ft.model')
del ft
# Load model
ft = FastText.load('./ft.model')
# Generate ngrams for test-word given min_n=3 and max_n=4
encoded_ngrams = [b"<he", b"<hel", b"hel", b"hell", b"ell", b"ello", b"llo", b"llo>", b"lo>"]
# Hash ngrams to its corresponding index, just as Gensim does
ngram_hashes = [ft_hash_bytes(n) % 100 for n in encoded_ngrams]
word_vec = np.zeros(5, dtype=np.float32)
for nh in ngram_hashes:
word_vec += ft.wv.vectors_ngrams[nh]
# Compare both arrays
print(np.isclose(ft.wv['hello'], word_vec))
The output of this script is False for every dimension of the compared arrays.
It would be nice if someone could point me out if i'm missing something or doing something wrong. Thanks in advance!
The calculation of a full word's FastText word-vector is not just the sum of its character n-gram vectors, but also a raw full-word vector that's also trained for in-vocabulary words.
The full-word vectors you get back from ft.wv[word] for known-words have already had this combination pre-calculated. See the adjust_vectors() method for an example of this full calculation:
https://github.com/RaRe-Technologies/gensim/blob/68ec5b8ed7f18e75e0b13689f4da53405ef3ed96/gensim/models/keyedvectors.py#L2282
The raw full-word vectors are in a .vectors_vocab array on the model.wv object.
(If this isn't enough to reconcile matters: ensure you're using the latest gensim, as there have been many recent FT fixes. And, ensure your list of ngram-hashes matches the output of the ft_ngram_hashes() method of the library – if not, your manual ngram-list-creation and subsequent hashing may be doing something different.)
i have a list of tokenized documents,containing both unigrams, bi-grams and i would like to perform sklearn lda on it.i have tried the following code:
my_data =[['low-rank matrix','detection method','problem finding'],['probabilistic inference','problem finding','statistical learning','solution' ],['detection method','probabilistic inference','population','language']...]
tf_vectorizer = CountVectorizer(min_df=2, max_features=n_features,
stop_words='english')
tf = tf_vectorizer.fit_transform(mydata)
lda = LatentDirichletAllocation(n_topics=3, max_iter=5,random_state=10)
but when i print the output i get something like this:
topic 0:
detection,finding, solution ,method,problem
topic 1:
language, statistical , problem, learning,finding
and so on..
bigrams are broken and are separated from one another.i have 10,000 documents and already tokenize them, also the method for finding the bigram is not nltk based so i already did this.
is there any method to improve this without changing the input?
i am very new in using sklearn so apologies in advance if i am making some obvious mistake.
CountVectorizer has a ngram_range param which will be used for deciding if the vocabulary will contain uniqrams, or bigrams or trigrams etc:-
ngram_range : tuple (min_n, max_n)
The lower and upper boundary of the
range of n-values for different n-grams to be extracted. All values of
n such that min_n <= n <= max_n will be used.
For example:
ngram_range=(1,1) => Will include only unigrams
ngram_range=(1,2) => Will include unigrams and bigrams
ngram_range=(2,2) => Will include only bigrams
and so on...
You have not defined that, so default ngram_range=(1,1) and hence only unigrams are used here.
tf_vectorizer = CountVectorizer(min_df=2,
max_features=n_features,
stop_words='english',
ngram_range = (2,2)) # You need this
tf = tf_vectorizer.fit_transform(my_data)
Secondly, you say that you have already tokenize the data and show the lists of list (my_data) in your code. That doesnt work with CountVectorizer. For that, you need to pass a simple list of strings and CountVectorizer will automatically apply tokenizing on them. So you will need to pass on your own preprocessing steps to that. See other params 'preprocessor', 'tokenizer' and 'analyzer' in the linked documentation.
I am using tfidfvectorizer to score terms from many different corpus.
Here is my code
tfidf = TfidfVectorizer(ngram_range=(1,1), stop_words = 'english', min_df = 0.5)
for corpus in all_corpus:
tfidf.fit_transform(corpus)
The number of documents in each corpus is various, so when building the vocabulary, some corpus remains empty and return an error:
after pruning, no terms remain. Try a lower min_df or higher max_df
I don't want to change the min or max DF. What I need is when there is no terms, the transforming process is skipped. So I made a conditional filter like below
for corpus in all_corpus:
tfidf.fit_transform(corpus)
if tfidf.shape[0] > 0:
\\execute some code here
However, the condition couldn't work. Is there way to fix this?
All answers and comments are really appreciated. Thanks
First, a minimum working example for your problem is I believe, the following:
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(ngram_range=(1,1), stop_words = 'english', min_df = 0.5)
tfidf.fit_transform(['not I you'])
I could not replicate an error message that contains the part of the error message you share, but this gives me a ValueError as all the words in my document are English stop words. (The code runs if one removes stop_words = 'english' in the snippet above.)
One way of handling the error in the case of a for-loop is to use a try/except block.
for corpus in all_corpus:
try:
tfidf.fit_transform(corpus)
except ValueError:
print('Transforming process skipped')
# Here you can do more stuff
continue # go to the beginning of the for-loop to start the next iteration
# Here goes the rest of the code for corpus for which the transform functioned