I would like to extract unique combinations of letters within words using the scikit-learn TF-IDF vectorizer for an NLP problem. However, I'm not interested in individual letters, but letter combinations, so that, e.g. "the" should produce "th" and "he" but not "t", "h" or "e". My understanding is I should be able to use ngram_range. However, using ngram_range=(2,3) is still returning unigrams.
Example:
from sklearn.feature_extraction.text import TfidfVectorizer
examples = ['The cat on the mat',
'Fast and bulbous']
tfidf = TfidfVectorizer(max_features=None,
analyzer='char_wb',
ngram_range=(2, 3))
data=tfidf.fit_transform(examples)
print(pd.DataFrame(data=data.todense(),
index=examples,
columns = tfidf.get_feature_names_out()))
gives me the 2- and 3-gram results as expected but also unigrams (i.e. I don't want "a", "b", etc.):
a an b bu c \
The cat on the mat 0.000000 0.000000 0.000000 0.000000 0.139994
Fast and bulbous 0.181053 0.181053 0.181053 0.181053 0.000000
ca f fa m ma ... \
The cat on the mat 0.139994 0.000000 0.000000 0.139994 0.139994 ...
Fast and bulbous 0.000000 0.181053 0.181053 0.000000 0.000000 ...
s st st t th \
The cat on the mat 0.000000 0.000000 0.000000 0.199213 0.279987
Fast and bulbous 0.181053 0.181053 0.181053 0.128821 0.000000
the ul ulb us us
The cat on the mat 0.279987 0.000000 0.000000 0.000000 0.000000
Fast and bulbous 0.000000 0.181053 0.181053 0.181053 0.181053
[2 rows x 53 columns]
I would've expected this output with ngram_range=(1,3) but not ngram_range=(2,3).
Edit:
I just noticed that "a" is extracted from "Fast and bulbous", presumably as it occurs as " a", i.e. with a space before the "a", but not in "The cat on the mat" as the "a" in "cat" is surrounded by "c" and "t". Likewise, "u" is not extracted as there is no space surrounding it in either text.
It seems like TfidfVectorizer is extracting bigrams including spaces. Is there a way to turn this off? (I though using analyzer='char_wb' searched within words rather than across words).
I constructed a callable to pass to analyzer. It is stolen from based on the function from the source code of TfidfVectorizer used when analyzer is set to 'char_wb':
def char_wb_ngrams(text_document, ngram_range):
"""Callable for TfidfVectorizer analyzer, based on _char_wb_ngrams from TfidfVectorizer source code at
https://github.com/scikit-learn/scikit-learn/blob/f3f51f9b6/sklearn/feature_extraction/text.py"""
ngrams = []
min_n, max_n = ngram_range
for w in text_document.lower().split():
# This line in _char_wb_ngrams pads words with spaces and needs to be removed:
#w = " " + w + " "
w_len = len(w)
for n in range(min_n, max_n + 1):
offset = 0
ngrams.append(w[offset : offset + n])
while offset + n < w_len:
offset += 1
ngrams.append(w[offset : offset + n])
if offset == 0: # count a short word (w_len < n) only once
break
return ngrams
This works on the example data from above:
from functools import partial
tfidf_no_space = TfidfVectorizer(max_features=None,
analyzer=partial(char_wb_ngrams, ngram_range=(2,3)),
ngram_range=(2, 3))
data=tfidf_no_space.fit_transform(examples)
print(pd.DataFrame(data=data.todense(),
index=examples,
columns = tfidf_no_space.get_feature_names_out()))
which yields
an and as ast at \
The cat on the mat 0.000000 0.000000 0.000000 0.000000 0.436436
Fast and bulbous 0.229416 0.229416 0.229416 0.229416 0.000000
bo bou bu bul ca ... \
The cat on the mat 0.000000 0.000000 0.000000 0.000000 0.218218 ...
Fast and bulbous 0.229416 0.229416 0.229416 0.229416 0.000000 ...
nd on ou ous st \
The cat on the mat 0.000000 0.218218 0.000000 0.000000 0.000000
Fast and bulbous 0.229416 0.000000 0.229416 0.229416 0.229416
th the ul ulb us
The cat on the mat 0.436436 0.436436 0.000000 0.000000 0.000000
Fast and bulbous 0.000000 0.000000 0.229416 0.229416 0.229416
[2 rows x 28 columns]
I'm not sure this would work with punctuation, though. It would be good to have a version that strips punctuation and also doesn't require the call to partial (which fixes ngram_range in the function).
I have two arrays. It looks like:
GarageMudroomLights [kW] DiningRoomOutlets [kW]
0 0.001159 0.000000
1 0.001223 0.000000
2 0.001281 0.000000
3 0.001525 0.000000
4 0.001549 0.000000
5 0.001490 0.000000
... ... ...
39796 0.003750 0.001783
39797 0.003717 0.001850
39798 0.003617 0.001867
I need to find principal components like two orthogonal vectors.
I've tried to get it with PCA in python, but I can't understand where is components there, where is the rotation angle between vectors (necessary vectors and angle (picture)) and how it works only with two arrays.
Now I'm standardized arrays and don't understand what's next.
import pandas
from sklearn.preprocessing import StandardScaler
s_i_t = pandas.read_csv('dataset.csv', sep=',')
s_i_t_std = StandardScaler().fit_transform(s_i_t)
Hi I have a lemmatized text in the format as shown by lemma. I want to get TfIdf score for each word this is the function that I wrote:
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
lemma=["'Ah", 'yes', u'say', 'softly', 'Harry',
'Potter', 'Our', 'new', 'celebrity', 'You',
'learn', 'subtle', 'science', 'exact', 'art',
'potion-making', u'begin', 'He', u'speak', 'barely',
'whisper', 'caught', 'every', 'word', 'like',
'Professor', 'McGonagall', 'Snape', 'gift',
u'keep', 'class', 'silent', 'without', 'effort',
'As', 'little', 'foolish', 'wand-waving', 'many',
'hardly', 'believe', 'magic', 'I', 'dont', 'expect', 'really',
'understand', 'beauty']
def Tfidf_Vectorize(lemmas_name):
vect = TfidfVectorizer(stop_words='english',ngram_range=(1,2))
vect_transform = vect.fit_transform(lemmas_name)
# First approach of creating a dataframe of weight & feature names
vect_score = np.asarray(vect_transform.mean(axis=0)).ravel().tolist()
vect_array = pd.DataFrame({'term': vect.get_feature_names(), 'weight': vect_score})
vect_array.sort_values(by='weight',ascending=False,inplace=True)
# Second approach of getting the feature names
vect_fn = np.array(vect.get_feature_names())
sorted_tfidf_index = vect_transform.max(0).toarray()[0].argsort()
print('Largest Tfidf:\n{}\n'.format(vect_fn[sorted_tfidf_index[:-11:-1]]))
return vect_array
tf_dataframe=Tfidf_Vectorize(lemma)
print(tf_dataframe.iloc[:5,:])
The output I am getting by:
print('Largest Tfidf:\n{}\n'.format(vect_fn[sorted_tfidf_index[:-11:-1]]))
is
Largest Tfidf:
[u'yes' u'fools' u'fury' u'gale' u'ghosts' u'gift' u'glory' u'glow' u'good'
u'granger']
The result of tf_dataframe
term weight
261 snape 0.027875
238 say 0.022648
211 potter 0.013937
181 mind 0.010453
123 harry 0.010453
60 dark 0.006969
75 dumbledore 0.006969
311 voice 0.005226
125 head 0.005226
231 ron 0.005226
Shouldn't both approaches lead to the same result of top features? I just want to calculate the tfidf scores and get the top 5 features/weight. What am i doing wrong?
I am not sure what I am looking at here but I have the feeling that you're using TfidfVectorizer incorrectly. However, please correct me in case I got the wrong idea of what you're trying.
So.. what you need is a list of documents which you feed to fit_transform(). From that you can construct a matrix where, for example, each column represents a document and each row a word. One cell in that matrix is the tf-idf score of the word i in document j.
Here's an example:
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
documents = [
"This is a document.",
"This is another document with slightly more text.",
"Whereas this is yet another document with even more text than the other ones.",
"This document is awesome and also rather long.",
"The car he drove was red."
]
document_names = ['Doc {:d}'.format(i) for i in range(len(documents))]
def get_tfidf(docs, ngram_range=(1,1), index=None):
vect = TfidfVectorizer(stop_words='english', ngram_range=ngram_range)
tfidf = vect.fit_transform(documents).todense()
return pd.DataFrame(tfidf, columns=vect.get_feature_names(), index=index).T
print(get_tfidf(documents, ngram_range=(1,2), index=document_names))
Which will give you:
Doc 0 Doc 1 Doc 2 Doc 3 Doc 4
awesome 0.0 0.000000 0.000000 0.481270 0.000000
awesome long 0.0 0.000000 0.000000 0.481270 0.000000
car 0.0 0.000000 0.000000 0.000000 0.447214
car drove 0.0 0.000000 0.000000 0.000000 0.447214
document 1.0 0.282814 0.282814 0.271139 0.000000
document awesome 0.0 0.000000 0.000000 0.481270 0.000000
document slightly 0.0 0.501992 0.000000 0.000000 0.000000
document text 0.0 0.000000 0.501992 0.000000 0.000000
drove 0.0 0.000000 0.000000 0.000000 0.447214
drove red 0.0 0.000000 0.000000 0.000000 0.447214
long 0.0 0.000000 0.000000 0.481270 0.000000
ones 0.0 0.000000 0.501992 0.000000 0.000000
red 0.0 0.000000 0.000000 0.000000 0.447214
slightly 0.0 0.501992 0.000000 0.000000 0.000000
slightly text 0.0 0.501992 0.000000 0.000000 0.000000
text 0.0 0.405004 0.405004 0.000000 0.000000
text ones 0.0 0.000000 0.501992 0.000000 0.000000
The two methods you show to get to words and their respective scores calculate the mean over all documents and fetch the max score of each word respectively.
So let's do this and compare the two methods:
df = get_tfidf(documents, ngram_range=(1,2), index=index)
print(pd.DataFrame([df.mean(1), df.max(1)], index=['score_mean', 'score_max']).T)
We can see that the scores are of course different.
score_mean score_max
awesome 0.096254 0.481270
awesome long 0.096254 0.481270
car 0.089443 0.447214
car drove 0.089443 0.447214
document 0.367353 1.000000
document awesome 0.096254 0.481270
document slightly 0.100398 0.501992
document text 0.100398 0.501992
drove 0.089443 0.447214
drove red 0.089443 0.447214
long 0.096254 0.481270
ones 0.100398 0.501992
red 0.089443 0.447214
slightly 0.100398 0.501992
slightly text 0.100398 0.501992
text 0.162002 0.405004
text ones 0.100398 0.501992
Note:
You can convince yourself that this does the same as calling min/max on the TfidfVectorizer:
vect = TfidfVectorizer(stop_words='english', ngram_range=(1,2))
tfidf = vect.fit_transform(documents)
print(tfidf.max(0))
print(tfidf.mean(0))
I need to read an ASCII file containing X and Y coordinates as well a Z value using Python. These will be written as features in a feature class in ArcMap. Each point makes up a polygon where each feature is seperated by a row containing '999.0 999.0 999.0' as shown in the example. I'm wondering what the best way is to seperate each feature as there is no feature ID column.
329462.713287 8981177.910780 0.000000
331660.441771 8981187.405700 0.000000
331669.945462 8978975.695090 0.000000
329472.340912 8978966.180280 0.000000
329462.713287 8981177.910780 0.000000
999.0 999.0 999.0
297517.590475 8981318.596530 0.000000
299715.649732 8981329.876880 0.000000
299726.953175 8979117.630860 0.000000
297529.017922 8979106.326860 0.000000
297517.590475 8981318.596530 0.000000
999.0 999.0 999.0
Simply iterate the data line by line, and check whether the line contains your magic triplet and when you catch that line increase the feature index.
I am trying to use nipype to analyze transformation matrixes that were created by FSL.
FSL has a script called "avscale" that analyzes those transformation matrixes (*.mat files).
I was wondering whether nipype has any interface that wrap that script and enable to work with its output.
Thanks
Based on the docs and the current source the answer is no. Also, avscale has also not been mentioned on the nipy-devel mailing list since at least last February. It's possible that Nipype already wraps something else that does this (perhaps with a matlab wrapper?) You could try opening an issue or asking the the mailing list.
As long as you're trying to use Python (with nipype and all), maybe the philosophy of the nipype project is that you should just use numpy/scipy for this? Just a guess, I don't know the functions to replicate this output with those tools. It's also possible that no one has gotten around to adding it yet.
For the uninitiated, avscale takes this affine matrix:
1.00614 -8.39414e-06 0 -0.757356
0 1.00511 -0.00317841 -0.412038
0 0.0019063 1.00735 -0.953364
0 0 0 1
and yields this or similar output:
Rotation & Translation Matrix:
1.000000 0.000000 0.000000 -0.757356
0.000000 0.999998 -0.001897 -0.412038
0.000000 0.001897 0.999998 -0.953364
0.000000 0.000000 0.000000 1.000000
Scales (x,y,z) = 1.006140 1.005112 1.007354
Skews (xy,xz,yz) = -0.000008 0.000000 -0.001259
Average scaling = 1.0062
Determinant = 1.01872
Left-Right orientation: preserved
Forward half transform =
1.003065 -0.000004 -0.000000 -0.378099
0.000000 1.002552 -0.001583 -0.206133
0.000000 0.000951 1.003669 -0.475711
0.000000 0.000000 0.000000 1.000000
Backward half transform =
0.996944 0.000004 0.000000 0.376944
0.000000 0.997452 0.001575 0.206357
0.000000 -0.000944 0.996343 0.473777
0.000000 0.000000 0.000000 1.000000