I tried to build the a very simple SVM predictor that I would understand with my basic python knowledge. As my code looks so different from this question and also this question I don't know how I can find the most important features for SVM prediction in my example.
I have the following 'sample' containing features and class (status):
A B C D E F status
1 5 2 5 1 3 1
1 2 3 2 2 1 0
3 4 2 3 5 1 1
1 2 2 1 1 4 0
I saved the feature names as 'features':
A B C D E F
The features 'X':
1 5 2 5 1 3
1 2 3 2 2 1
3 4 2 3 5 1
1 2 2 1 1 4
And the status 'y':
1
0
1
0
Then I build X and y arrays out of the sample, train & test on half of the sample and count the correct predictions.
import pandas as pd
import numpy as np
from sklearn import svm
X = np.array(sample[features].values)
X = preprocessing.scale(X)
X = np.array(X)
y = sample['status'].values.tolist()
y = np.array(y)
test_size = int(X.shape[0]/2)
clf = svm.SVC(kernel="linear", C= 1)
clf.fit(X[:-test_size],y[:-test_size])
correct_count = 0
for x in range(1, test_size+1):
if clf.predict(X[-x].reshape(-1, len(features)))[0] == y[-x]:
correct_count += 1
accuracy = (float(correct_count)/test_size) * 100.00
My problem is now, that I have no idea, how I could implement the code from the questions above so that I could also see, which ones are the most important features.
I would be grateful if you could tell me, if that's even possible for my simple version? And if yes, any tipps on how to do it would be great.
From all feature set, the set of variables which produces the lowest values for square of norm of vector must be chosen as variables of high importance in order
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As part of my assignment I am building logistic regression model but I am getting an error "Perfect separation detected, results not available" while building it.
**X_train :-**
year amt_spnt rank
1 -1.723034 -0.418500 0.272727
2 0.716660 2.088507 -0.636364
3 1.174102 -0.558333 -1.545455
4 -0.503187 -1.297451 1.181818
5 1.326583 -0.628250 -1.545455
**y_train :-**
1 0
2 1
3 1
4 0
5 1
Name: result, dtype: int64
**Logistic Model code:-**
import statsmodels.api as sm
logm1 = sm.GLM(y_train,(sm.add_constant(X_train)), family = sm.families.Binomial())
logm1.fit().summary()
**Dataset before and after scaling**
**Image for evidence:-**
[![Evidence][1]][1]
[1]: https://i.stack.imgur.com/cTncA.png
This is a model setting issue, because of the perfect separation, your model can not converge. Perfect separation means there is one (or more) variable in your independent variables that can perfectly distinct dependent variable = 0 from dependent variable = 1. See the following example:
Y 0 0 0 0 0 0 1 1 1 1
X 1 2 3 4 4 4 5 6 7 8
If X <= 4, Y = 0
If X > 4, Y = 1
A short answer to your question is to find such variable in your independent variable and remove it from your model.
I have a dataset df of object components in 3-d space - each ID represents an object which has various components:
ID Comp x y z
A 1 2 2 1
A 2 2 1 -1
A 3 -10 1 -10
A 4 -1 3 -5
B 1 3 0 0
B 2 3 0 -5
...
I would like to loop through each ID, using a clustering technique in sklearn to create clusters of components (Comp) based on each component's (x,y,z) co-ordinates - to achieve something like this:
ID Comp x y z cluster
A 1 2 2 1 1
A 2 2 1 -1 1
A 3 -10 1 -10 2
A 4 -1 3 -5 3
B 1 3 0 0 1
B 2 3 0 -5 1
...
As an example - ID:A,Comp:1 is incluster1, whereasID:A, Comp:4 is in cluster 3. (I plan to then concatenate ID and cluster later).
I'm having no luck with the following groupby + apply:
from sklearn.cluster import AffinityPropagation
ap = AffinityPropagation()
df['cluster']=df.groupby(['ID','Comp']).apply(lambda x: ap.fit_predict(np.array([x.x,x.y,x.z]).T))
I could brute-force it by using a for loop over the ID but my dataset is large (~ 150k ID) and I'm worried about resource and time constraints. Any help would be great!
IIUC, I think you could try something like this:
def ap_fit_pred(x):
ap = AffinityPropagation()
return pd.Series(ap.fit_predict(x.loc[:,['x','y','z']]))
df['cluster'] = df.groupby('ID').apply(ap_fit_pred).reset_index(drop=True)
I have more than 2500 samples on which static analysis has been performed, with more than 300 features extracted per sample.
Among these samples, I have discriminated more than 10 APT class and my aim is to build, for each class, a one-class classifier.
I'm using python scikit library for machine-learning, and in particular i'm facing with One-class SVM.
First question: There exist some other good one-class classifier for this approach?
Second question: I have to come up with some metrics that can define a sort of "accuracy" of the classifier. Now I know that for one-class SVM the accuracy concept is not so well-define. I report my code and my concept:
import numpy as np
import pandas as pd
from sklearn import svm
from sklearn.model_selection import train_test_split
df = pd.read_csv('features_labeled_apt17.csv')
X = df.ix[:,1:341].values
X_train, X_test = train_test_split(X,test_size = 0.3,random_state = 42)
clf = svm.OneClassSVM(nu=0.1,kernel = "linear", gamma =0.1)
y_score = clf.fit(X_train)
pred = clf.predict(X_test)
print(pred)
These represents the output of the code:
[ 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 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 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]
The 1 represent of course the well-labeled sample, while the -1 represent the wrong one.
First: do you think this can be a good approach?
Second: For metrics, if I divide the total element in the testing set by the wrong labeled?
In my understanding in machine learning algorithms, your use case is not a good one to apply oneclass-SVM classifier.
Normally, oneclass-svm is used for Unsupervised Outlier Detection problems. Refer this page to see the implementation of oneclass-svm to detect outliers.
Just display your data-frame, I will find any new approach to solve your problem.
I am trying to write a progrma in Spark for carrying out Latent Dirichlet allocation (LDA). This Spark documentation page provides a nice example for perfroming LDA on the sample data. Below is the program
from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.mllib.linalg import Vectors
# Load and parse the data
data = sc.textFile("data/mllib/sample_lda_data.txt")
parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
# Index documents with unique IDs
corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
# Cluster the documents into three topics using LDA
ldaModel = LDA.train(corpus, k=3)
# Output topics. Each is a distribution over words (matching word count vectors)
print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize())
+ " words):")
topics = ldaModel.topicsMatrix()
for topic in range(3):
print("Topic " + str(topic) + ":")
for word in range(0, ldaModel.vocabSize()):
print(" " + str(topics[word][topic]))
# Save and load model
ldaModel.save(sc, "target/org/apache/spark/PythonLatentDirichletAllocationExample/LDAModel")
sameModel = LDAModel\
.load(sc, "target/org/apache/spark/PythonLatentDirichletAllocationExample/LDAModel")
The sample input (sample_lda_data.txt) used is as below
1 2 6 0 2 3 1 1 0 0 3
1 3 0 1 3 0 0 2 0 0 1
1 4 1 0 0 4 9 0 1 2 0
2 1 0 3 0 0 5 0 2 3 9
3 1 1 9 3 0 2 0 0 1 3
4 2 0 3 4 5 1 1 1 4 0
2 1 0 3 0 0 5 0 2 2 9
1 1 1 9 2 1 2 0 0 1 3
4 4 0 3 4 2 1 3 0 0 0
2 8 2 0 3 0 2 0 2 7 2
1 1 1 9 0 2 2 0 0 3 3
4 1 0 0 4 5 1 3 0 1 0
How do I modify the program to run in on a text data file containing text data instead of numbers? Let the sample file contain the following text.
Latent Dirichlet allocation (LDA) is a topic model which infers
topics from a collection of text documents. LDA can be thought of as a
clustering algorithm as follows:
Topics correspond to cluster centers, and documents correspond to
examples (rows) in a dataset. Topics and documents both exist in a
feature space, where feature vectors are vectors of word counts (bag
of words). Rather than estimating a clustering using a traditional
distance, LDA uses a function based on a statistical model of how text
documents are generated.
After doing some research, I am attempting to answer this question. Below is the sample code to perform LDA on a text document with real text data using Spark.
from pyspark.sql import SQLContext, Row
from pyspark.ml.feature import CountVectorizer
from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.mllib.linalg import Vector, Vectors
path = "sample_text_LDA.txt"
data = sc.textFile(path).zipWithIndex().map(lambda (words,idd): Row(idd= idd, words = words.split(" ")))
docDF = spark.createDataFrame(data)
Vector = CountVectorizer(inputCol="words", outputCol="vectors")
model = Vector.fit(docDF)
result = model.transform(docDF)
corpus = result.select("idd", "vectors").rdd.map(lambda (x,y): [x,Vectors.fromML(y)]).cache()
# Cluster the documents into three topics using LDA
ldaModel = LDA.train(corpus, k=3,maxIterations=100,optimizer='online')
topics = ldaModel.topicsMatrix()
vocabArray = model.vocabulary
wordNumbers = 10 # number of words per topic
topicIndices = sc.parallelize(ldaModel.describeTopics(maxTermsPerTopic = wordNumbers))
def topic_render(topic): # specify vector id of words to actual words
terms = topic[0]
result = []
for i in range(wordNumbers):
term = vocabArray[terms[i]]
result.append(term)
return result
topics_final = topicIndices.map(lambda topic: topic_render(topic)).collect()
for topic in range(len(topics_final)):
print ("Topic" + str(topic) + ":")
for term in topics_final[topic]:
print (term)
print ('\n')
The topics extracted on the text data as mentioned in the question is as below:
For my thesis i am working on a machine learning project using Python which includes feature extraction from text. As a start I am trying to implement bi-grams using sci-kit learn.
Right now, when i process my data trough Countvectorizer, I get an array of just 1's and sometimes a bit more. E.g.:
`[[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 1 1]]`
I want to use these bi-grams to predict my target variable, which is categorical.
When i now execute my code, Python returns that the shape of my two arrays are not identical.
`[[1 3 2 ..., 1 1 1]] [ 0. 0. 1. 0. 0.]`
Can someone tell me what i am doing wrong? I am using this command for the bi-grams. The first part is a loop for every text (film plot) in the dataset.
plottext = [ row[8] ]
wordvec = CountVectorizer(ngram_range=(2,2), analyzer='word')
plotvec = wordvec.fit_transform(plottext).toarray()
matrix_terms = np.array(wordvec.get_feature_names())
matrix_freq = np.asarray(plotvec.sum(axis=0)).ravel()
final_matrix = np.array([matrix_terms,matrix_freq])
target = { 'Age': row[4] }
data.append((final_matrix, target))
# Convert categorial target variable to Y
(X, Ycat) = zip(*data)
vec = DictVectorizer(sparse=False)
Y = vec.fit_transform(Ycat)
#Extract textual features from plot
return (X, Y)
The error message i get
ValueError: could not broadcast input array from shape (2,830) into shape (2)