- Model building in scikit-learn (refresher)
- Representing text as numerical data
- Reading a text-based dataset into pandas
- Vectorizing our dataset
- Building and evaluating a model
- Comparing models
- Examining a model for further insight
- Practicing this workflow on another dataset
- Tuning the vectorizer (discussion)
# for Python 2: use print only as a function
from __future__ import print_function# load the iris dataset as an example
from sklearn.datasets import load_iris
iris = load_iris()# store the feature matrix (X) and response vector (y)
X = iris.data
y = iris.target"Features" are also known as predictors, inputs, or attributes. The "response" is also known as the target, label, or output.
# check the shapes of X and y
print(X.shape)
print(y.shape)(150L, 4L)
(150L,)
"Observations" are also known as samples, instances, or records.
# examine the first 5 rows of the feature matrix (including the feature names)
import pandas as pd
pd.DataFrame(X, columns=iris.feature_names).head()| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
# examine the response vector
print(y)[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]
In order to build a model, the features must be numeric, and every observation must have the same features in the same order.
# import the class
from sklearn.neighbors import KNeighborsClassifier
# instantiate the model (with the default parameters)
knn = KNeighborsClassifier()
# fit the model with data (occurs in-place)
knn.fit(X, y)KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
In order to make a prediction, the new observation must have the same features as the training observations, both in number and meaning.
# predict the response for a new observation
knn.predict([[3, 5, 4, 2]])array([1])
# example text for model training (SMS messages)
simple_train = ['call you tonight', 'Call me a cab', 'please call me... PLEASE!']From the scikit-learn documentation:
Text Analysis is a major application field for machine learning algorithms. However the raw data, a sequence of symbols cannot be fed directly to the algorithms themselves as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length.
We will use CountVectorizer to "convert text into a matrix of token counts":
# import and instantiate CountVectorizer (with the default parameters)
from sklearn.feature_extraction.text import CountVectorizer
vect = CountVectorizer()# learn the 'vocabulary' of the training data (occurs in-place)
vect.fit(simple_train)CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
tokenizer=None, vocabulary=None)
# examine the fitted vocabulary
vect.get_feature_names()[u'cab', u'call', u'me', u'please', u'tonight', u'you']
# transform training data into a 'document-term matrix'
simple_train_dtm = vect.transform(simple_train)
simple_train_dtm<3x6 sparse matrix of type '<type 'numpy.int64'>'
with 9 stored elements in Compressed Sparse Row format>
# convert sparse matrix to a dense matrix
simple_train_dtm.toarray()array([[0, 1, 0, 0, 1, 1],
[1, 1, 1, 0, 0, 0],
[0, 1, 1, 2, 0, 0]], dtype=int64)
# examine the vocabulary and document-term matrix together
pd.DataFrame(simple_train_dtm.toarray(), columns=vect.get_feature_names())| cab | call | me | please | tonight | you | |
|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 0 | 0 | 1 | 1 |
| 1 | 1 | 1 | 1 | 0 | 0 | 0 |
| 2 | 0 | 1 | 1 | 2 | 0 | 0 |
From the scikit-learn documentation:
In this scheme, features and samples are defined as follows:
- Each individual token occurrence frequency (normalized or not) is treated as a feature.
- The vector of all the token frequencies for a given document is considered a multivariate sample.
A corpus of documents can thus be represented by a matrix with one row per document and one column per token (e.g. word) occurring in the corpus.
We call vectorization the general process of turning a collection of text documents into numerical feature vectors. This specific strategy (tokenization, counting and normalization) is called the Bag of Words or "Bag of n-grams" representation. Documents are described by word occurrences while completely ignoring the relative position information of the words in the document.
# check the type of the document-term matrix
type(simple_train_dtm)scipy.sparse.csr.csr_matrix
# examine the sparse matrix contents
print(simple_train_dtm) (0, 1) 1
(0, 4) 1
(0, 5) 1
(1, 0) 1
(1, 1) 1
(1, 2) 1
(2, 1) 1
(2, 2) 1
(2, 3) 2
From the scikit-learn documentation:
As most documents will typically use a very small subset of the words used in the corpus, the resulting matrix will have many feature values that are zeros (typically more than 99% of them).
For instance, a collection of 10,000 short text documents (such as emails) will use a vocabulary with a size in the order of 100,000 unique words in total while each document will use 100 to 1000 unique words individually.
In order to be able to store such a matrix in memory but also to speed up operations, implementations will typically use a sparse representation such as the implementations available in the
scipy.sparsepackage.
# example text for model testing
simple_test = ["please don't call me"]In order to make a prediction, the new observation must have the same features as the training observations, both in number and meaning.
# transform testing data into a document-term matrix (using existing vocabulary)
simple_test_dtm = vect.transform(simple_test)
simple_test_dtm.toarray()array([[0, 1, 1, 1, 0, 0]], dtype=int64)
# examine the vocabulary and document-term matrix together
pd.DataFrame(simple_test_dtm.toarray(), columns=vect.get_feature_names())| cab | call | me | please | tonight | you | |
|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 1 | 1 | 0 | 0 |
Summary:
vect.fit(train)learns the vocabulary of the training datavect.transform(train)uses the fitted vocabulary to build a document-term matrix from the training datavect.transform(test)uses the fitted vocabulary to build a document-term matrix from the testing data (and ignores tokens it hasn't seen before)
# read file into pandas using a relative path
path = 'data/sms.tsv'
sms = pd.read_table(path, header=None, names=['label', 'message'])# alternative: read file into pandas from a URL
# url = 'https://raw.githubusercontent.com/justmarkham/pycon-2016-tutorial/master/data/sms.tsv'
# sms = pd.read_table(url, header=None, names=['label', 'message'])# examine the shape
sms.shape(5572, 2)
# examine the first 10 rows
sms.head(10)| label | message | |
|---|---|---|
| 0 | ham | Go until jurong point, crazy.. Available only ... |
| 1 | ham | Ok lar... Joking wif u oni... |
| 2 | spam | Free entry in 2 a wkly comp to win FA Cup fina... |
| 3 | ham | U dun say so early hor... U c already then say... |
| 4 | ham | Nah I don't think he goes to usf, he lives aro... |
| 5 | spam | FreeMsg Hey there darling it's been 3 week's n... |
| 6 | ham | Even my brother is not like to speak with me. ... |
| 7 | ham | As per your request 'Melle Melle (Oru Minnamin... |
| 8 | spam | WINNER!! As a valued network customer you have... |
| 9 | spam | Had your mobile 11 months or more? U R entitle... |
# examine the class distribution
sms.label.value_counts()ham 4825
spam 747
Name: label, dtype: int64
# convert label to a numerical variable
sms['label_num'] = sms.label.map({'ham':0, 'spam':1})# check that the conversion worked
sms.head(10)| label | message | label_num | |
|---|---|---|---|
| 0 | ham | Go until jurong point, crazy.. Available only ... | 0 |
| 1 | ham | Ok lar... Joking wif u oni... | 0 |
| 2 | spam | Free entry in 2 a wkly comp to win FA Cup fina... | 1 |
| 3 | ham | U dun say so early hor... U c already then say... | 0 |
| 4 | ham | Nah I don't think he goes to usf, he lives aro... | 0 |
| 5 | spam | FreeMsg Hey there darling it's been 3 week's n... | 1 |
| 6 | ham | Even my brother is not like to speak with me. ... | 0 |
| 7 | ham | As per your request 'Melle Melle (Oru Minnamin... | 0 |
| 8 | spam | WINNER!! As a valued network customer you have... | 1 |
| 9 | spam | Had your mobile 11 months or more? U R entitle... | 1 |
# how to define X and y (from the iris data) for use with a MODEL
X = iris.data
y = iris.target
print(X.shape)
print(y.shape)(150L, 4L)
(150L,)
# how to define X and y (from the SMS data) for use with COUNTVECTORIZER
X = sms.message
y = sms.label_num
print(X.shape)
print(y.shape)(5572L,)
(5572L,)
# split X and y into training and testing sets
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)(4179L,)
(1393L,)
(4179L,)
(1393L,)
# instantiate the vectorizer
vect = CountVectorizer()# learn training data vocabulary, then use it to create a document-term matrix
vect.fit(X_train)
X_train_dtm = vect.transform(X_train)# equivalently: combine fit and transform into a single step
X_train_dtm = vect.fit_transform(X_train)# examine the document-term matrix
X_train_dtm<4179x7456 sparse matrix of type '<type 'numpy.int64'>'
with 55209 stored elements in Compressed Sparse Row format>
# transform testing data (using fitted vocabulary) into a document-term matrix
X_test_dtm = vect.transform(X_test)
X_test_dtm<1393x7456 sparse matrix of type '<type 'numpy.int64'>'
with 17604 stored elements in Compressed Sparse Row format>
We will use multinomial Naive Bayes:
The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
# import and instantiate a Multinomial Naive Bayes model
from sklearn.naive_bayes import MultinomialNB
nb = MultinomialNB()# train the model using X_train_dtm (timing it with an IPython "magic command")
%time nb.fit(X_train_dtm, y_train)Wall time: 3 ms
MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)
# make class predictions for X_test_dtm
y_pred_class = nb.predict(X_test_dtm)# calculate accuracy of class predictions
from sklearn import metrics
metrics.accuracy_score(y_test, y_pred_class)0.98851399856424982
# print the confusion matrix
metrics.confusion_matrix(y_test, y_pred_class)array([[1203, 5],
[ 11, 174]])
# print message text for the false positives (ham incorrectly classified as spam)
X_test[y_test < y_pred_class]574 Waiting for your call.
3375 Also andros ice etc etc
45 No calls..messages..missed calls
3415 No pic. Please re-send.
1988 No calls..messages..missed calls
Name: message, dtype: object
# print message text for the false negatives (spam incorrectly classified as ham)
X_test[y_test > y_pred_class]3132 LookAtMe!: Thanks for your purchase of a video...
5 FreeMsg Hey there darling it's been 3 week's n...
3530 Xmas & New Years Eve tickets are now on sale f...
684 Hi I'm sue. I am 20 years old and work as a la...
1875 Would you like to see my XXX pics they are so ...
1893 CALL 09090900040 & LISTEN TO EXTREME DIRTY LIV...
4298 thesmszone.com lets you send free anonymous an...
4949 Hi this is Amy, we will be sending you a free ...
2821 INTERFLORA - �It's not too late to order Inter...
2247 Hi ya babe x u 4goten bout me?' scammers getti...
4514 Money i have won wining number 946 wot do i do...
Name: message, dtype: object
# example false negative
X_test[3132]"LookAtMe!: Thanks for your purchase of a video clip from LookAtMe!, you've been charged 35p. Think you can do better? Why not send a video in a MMSto 32323."
# calculate predicted probabilities for X_test_dtm (poorly calibrated)
y_pred_prob = nb.predict_proba(X_test_dtm)[:, 1]
y_pred_probarray([ 2.87744864e-03, 1.83488846e-05, 2.07301295e-03, ...,
1.09026171e-06, 1.00000000e+00, 3.98279868e-09])
# calculate AUC
metrics.roc_auc_score(y_test, y_pred_prob)0.98664310005369604
We will compare multinomial Naive Bayes with logistic regression:
Logistic regression, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.
# import and instantiate a logistic regression model
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()# train the model using X_train_dtm
%time logreg.fit(X_train_dtm, y_train)Wall time: 39 ms
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
# make class predictions for X_test_dtm
y_pred_class = logreg.predict(X_test_dtm)# calculate predicted probabilities for X_test_dtm (well calibrated)
y_pred_prob = logreg.predict_proba(X_test_dtm)[:, 1]
y_pred_probarray([ 0.01269556, 0.00347183, 0.00616517, ..., 0.03354907,
0.99725053, 0.00157706])
# calculate accuracy
metrics.accuracy_score(y_test, y_pred_class)0.9877961234745154
# calculate AUC
metrics.roc_auc_score(y_test, y_pred_prob)0.99368176123143015
We will examine the our trained Naive Bayes model to calculate the approximate "spamminess" of each token.
# store the vocabulary of X_train
X_train_tokens = vect.get_feature_names()
len(X_train_tokens)7456
# examine the first 50 tokens
print(X_train_tokens[0:50])[u'00', u'000', u'008704050406', u'0121', u'01223585236', u'01223585334', u'0125698789', u'02', u'0207', u'02072069400', u'02073162414', u'02085076972', u'021', u'03', u'04', u'0430', u'05', u'050703', u'0578', u'06', u'07', u'07008009200', u'07090201529', u'07090298926', u'07123456789', u'07732584351', u'07734396839', u'07742676969', u'0776xxxxxxx', u'07781482378', u'07786200117', u'078', u'07801543489', u'07808', u'07808247860', u'07808726822', u'07815296484', u'07821230901', u'07880867867', u'0789xxxxxxx', u'07946746291', u'0796xxxxxx', u'07973788240', u'07xxxxxxxxx', u'08', u'0800', u'08000407165', u'08000776320', u'08000839402', u'08000930705']
# examine the last 50 tokens
print(X_train_tokens[-50:])[u'yer', u'yes', u'yest', u'yesterday', u'yet', u'yetunde', u'yijue', u'ym', u'ymca', u'yo', u'yoga', u'yogasana', u'yor', u'yorge', u'you', u'youdoing', u'youi', u'youphone', u'your', u'youre', u'yourjob', u'yours', u'yourself', u'youwanna', u'yowifes', u'yoyyooo', u'yr', u'yrs', u'ything', u'yummmm', u'yummy', u'yun', u'yunny', u'yuo', u'yuou', u'yup', u'zac', u'zaher', u'zealand', u'zebra', u'zed', u'zeros', u'zhong', u'zindgi', u'zoe', u'zoom', u'zouk', u'zyada', u'\xe8n', u'\u3028ud']
# Naive Bayes counts the number of times each token appears in each class
nb.feature_count_array([[ 0., 0., 0., ..., 1., 1., 1.],
[ 5., 23., 2., ..., 0., 0., 0.]])
# rows represent classes, columns represent tokens
nb.feature_count_.shape(2L, 7456L)
# number of times each token appears across all HAM messages
ham_token_count = nb.feature_count_[0, :]
ham_token_countarray([ 0., 0., 0., ..., 1., 1., 1.])
# number of times each token appears across all SPAM messages
spam_token_count = nb.feature_count_[1, :]
spam_token_countarray([ 5., 23., 2., ..., 0., 0., 0.])
# create a DataFrame of tokens with their separate ham and spam counts
tokens = pd.DataFrame({'token':X_train_tokens, 'ham':ham_token_count, 'spam':spam_token_count}).set_index('token')
tokens.head()| ham | spam | |
|---|---|---|
| token | ||
| 00 | 0 | 5 |
| 000 | 0 | 23 |
| 008704050406 | 0 | 2 |
| 0121 | 0 | 1 |
| 01223585236 | 0 | 1 |
# examine 5 random DataFrame rows
tokens.sample(5, random_state=6)| ham | spam | |
|---|---|---|
| token | ||
| very | 64 | 2 |
| nasty | 1 | 1 |
| villa | 0 | 1 |
| beloved | 1 | 0 |
| textoperator | 0 | 2 |
# Naive Bayes counts the number of observations in each class
nb.class_count_array([ 3617., 562.])
Before we can calculate the "spamminess" of each token, we need to avoid dividing by zero and account for the class imbalance.
# add 1 to ham and spam counts to avoid dividing by 0
tokens['ham'] = tokens.ham + 1
tokens['spam'] = tokens.spam + 1
tokens.sample(5, random_state=6)| ham | spam | |
|---|---|---|
| token | ||
| very | 65 | 3 |
| nasty | 2 | 2 |
| villa | 1 | 2 |
| beloved | 2 | 1 |
| textoperator | 1 | 3 |
# convert the ham and spam counts into frequencies
tokens['ham'] = tokens.ham / nb.class_count_[0]
tokens['spam'] = tokens.spam / nb.class_count_[1]
tokens.sample(5, random_state=6)| ham | spam | |
|---|---|---|
| token | ||
| very | 0.017971 | 0.005338 |
| nasty | 0.000553 | 0.003559 |
| villa | 0.000276 | 0.003559 |
| beloved | 0.000553 | 0.001779 |
| textoperator | 0.000276 | 0.005338 |
# calculate the ratio of spam-to-ham for each token
tokens['spam_ratio'] = tokens.spam / tokens.ham
tokens.sample(5, random_state=6)| ham | spam | spam_ratio | |
|---|---|---|---|
| token | |||
| very | 0.017971 | 0.005338 | 0.297044 |
| nasty | 0.000553 | 0.003559 | 6.435943 |
| villa | 0.000276 | 0.003559 | 12.871886 |
| beloved | 0.000553 | 0.001779 | 3.217972 |
| textoperator | 0.000276 | 0.005338 | 19.307829 |
# examine the DataFrame sorted by spam_ratio
# note: use sort() instead of sort_values() for pandas 0.16.2 and earlier
tokens.sort_values('spam_ratio', ascending=False)| ham | spam | spam_ratio | |
|---|---|---|---|
| token | |||
| claim | 0.000276 | 0.158363 | 572.798932 |
| prize | 0.000276 | 0.135231 | 489.131673 |
| 150p | 0.000276 | 0.087189 | 315.361210 |
| tone | 0.000276 | 0.085409 | 308.925267 |
| guaranteed | 0.000276 | 0.076512 | 276.745552 |
| 18 | 0.000276 | 0.069395 | 251.001779 |
| cs | 0.000276 | 0.065836 | 238.129893 |
| www | 0.000553 | 0.129893 | 234.911922 |
| 1000 | 0.000276 | 0.056940 | 205.950178 |
| awarded | 0.000276 | 0.053381 | 193.078292 |
| 150ppm | 0.000276 | 0.051601 | 186.642349 |
| uk | 0.000553 | 0.099644 | 180.206406 |
| 500 | 0.000276 | 0.048043 | 173.770463 |
| ringtone | 0.000276 | 0.044484 | 160.898577 |
| 000 | 0.000276 | 0.042705 | 154.462633 |
| mob | 0.000276 | 0.042705 | 154.462633 |
| co | 0.000553 | 0.078292 | 141.590747 |
| collection | 0.000276 | 0.039146 | 141.590747 |
| valid | 0.000276 | 0.037367 | 135.154804 |
| 2000 | 0.000276 | 0.037367 | 135.154804 |
| 800 | 0.000276 | 0.037367 | 135.154804 |
| 10p | 0.000276 | 0.037367 | 135.154804 |
| 8007 | 0.000276 | 0.035587 | 128.718861 |
| 16 | 0.000553 | 0.067616 | 122.282918 |
| weekly | 0.000276 | 0.033808 | 122.282918 |
| tones | 0.000276 | 0.032028 | 115.846975 |
| land | 0.000276 | 0.032028 | 115.846975 |
| http | 0.000276 | 0.032028 | 115.846975 |
| national | 0.000276 | 0.030249 | 109.411032 |
| 5000 | 0.000276 | 0.030249 | 109.411032 |
| ... | ... | ... | ... |
| went | 0.012718 | 0.001779 | 0.139912 |
| ll | 0.052530 | 0.007117 | 0.135494 |
| told | 0.013824 | 0.001779 | 0.128719 |
| feel | 0.013824 | 0.001779 | 0.128719 |
| gud | 0.014100 | 0.001779 | 0.126195 |
| cos | 0.014929 | 0.001779 | 0.119184 |
| but | 0.090683 | 0.010676 | 0.117731 |
| amp | 0.015206 | 0.001779 | 0.117017 |
| something | 0.015206 | 0.001779 | 0.117017 |
| sure | 0.015206 | 0.001779 | 0.117017 |
| ok | 0.061100 | 0.007117 | 0.116488 |
| said | 0.016312 | 0.001779 | 0.109084 |
| morning | 0.016865 | 0.001779 | 0.105507 |
| yeah | 0.017694 | 0.001779 | 0.100562 |
| lol | 0.017694 | 0.001779 | 0.100562 |
| anything | 0.017971 | 0.001779 | 0.099015 |
| my | 0.150401 | 0.014235 | 0.094646 |
| doing | 0.019077 | 0.001779 | 0.093275 |
| way | 0.019630 | 0.001779 | 0.090647 |
| ask | 0.019630 | 0.001779 | 0.090647 |
| already | 0.019630 | 0.001779 | 0.090647 |
| too | 0.021841 | 0.001779 | 0.081468 |
| come | 0.048936 | 0.003559 | 0.072723 |
| later | 0.030688 | 0.001779 | 0.057981 |
| lor | 0.032900 | 0.001779 | 0.054084 |
| da | 0.032900 | 0.001779 | 0.054084 |
| she | 0.035665 | 0.001779 | 0.049891 |
| he | 0.047000 | 0.001779 | 0.037858 |
| lt | 0.064142 | 0.001779 | 0.027741 |
| gt | 0.064971 | 0.001779 | 0.027387 |
7456 rows × 3 columns
# look up the spam_ratio for a given token
tokens.loc['dating', 'spam_ratio']83.667259786476862
Please open the exercise.ipynb notebook (or the exercise.py script).
Thus far, we have been using the default parameters of CountVectorizer:
# show default parameters for CountVectorizer
vectCountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
tokenizer=None, vocabulary=None)
However, the vectorizer is worth tuning, just like a model is worth tuning! Here are a few parameters that you might want to tune:
- stop_words: string {'english'}, list, or None (default)
- If 'english', a built-in stop word list for English is used.
- If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens.
- If None, no stop words will be used.
# remove English stop words
vect = CountVectorizer(stop_words='english')- ngram_range: tuple (min_n, max_n), default=(1, 1)
- 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.
# include 1-grams and 2-grams
vect = CountVectorizer(ngram_range=(1, 2))- max_df: float in range [0.0, 1.0] or int, default=1.0
- When building the vocabulary, ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words).
- If float, the parameter represents a proportion of documents.
- If integer, the parameter represents an absolute count.
# ignore terms that appear in more than 50% of the documents
vect = CountVectorizer(max_df=0.5)- min_df: float in range [0.0, 1.0] or int, default=1
- When building the vocabulary, ignore terms that have a document frequency strictly lower than the given threshold. (This value is also called "cut-off" in the literature.)
- If float, the parameter represents a proportion of documents.
- If integer, the parameter represents an absolute count.
# only keep terms that appear in at least 2 documents
vect = CountVectorizer(min_df=2)Guidelines for tuning CountVectorizer:
- Use your knowledge of the problem and the text, and your understanding of the tuning parameters, to help you decide what parameters to tune and how to tune them.
- Experiment, and let the data tell you the best approach!