I am new to dtreeviz.
I am struggling with a very deep decision tree that is very difficult to visualize (overfitting is not an issue for my task). I would like to know if there is a way to visualize only some nodes of the three (e.g., first 5 nodes).
Thanks!
from dtreeviz.models.xgb_decision_tree import ShadowXGBDTree
from dtreeviz import trees
xgb_shadow = ShadowXGBDTree(xgb_model_reg, 0, d.loc[:, d.columns != output_quantitativi[0]],
d[output_quantitativi[0]], d.loc[:, d.columns != output_quantitativi[0]].columns,output_quantitativi[0])
trees.dtreeviz(xgb_shadow)
for dtreeviz method it was just added the parameter depth_range_to_display, which allows you to specify a range of tree levels that you want to display.
For viz_leaf_samples() you can play with min_samples and max_samples values if the tree contains a lot of leaf nodes.
Related
I have two DiGraphs, say G and H, and I would like to count how many paths of G are part of H.
For any node pairs (src, dst) I can generate the paths between them using the 'all_simple_paths' function to get the generators:
G_gen = nx.all_simple_paths(G, src, dst)
H_gen = nx.all_simple_paths(H, src, dst)
Since the amount of paths is considerably high (the graphs have typically 100 nodes) I cannot resort to building lists etc.. (e.g. list(G_gen)) so I am wondering if there are smarter ways to deal with it. In addition, I would also like to distinguish based on the path lengths.
.. or maybe a better solution can be found with a different module ?
Thanks in advance for any help on this.
Thierry
I wonder if there is some reason why nx.intersection (see here) wouldn't work here? I'm not sure if it checks for direction under the hood but it doesn't seem to force outputs to standard Graph output either. Below might work:
# Create a couple of random preferential attachment graphs
G = nx.barabasi_albert_graph(100, 5)
H = nx.barabasi_albert_graph(100, 5)
# Convert to directed
G = G.to_directed()
H = H.to_directed()
# Get intersection
intersection = nx.intersection(G, H)
# Print info for each
print(nx.info(G))
print(nx.info(H))
print(nx.info(intersection))
which outputs:
>>> DiGraph with 100 nodes and 950 edges
>>> DiGraph with 100 nodes and 950 edges
>>> DiGraph with 100 nodes and 176 edges
The nodes are all shared in the example since the node ids are just simple integers and so they follow the same generation index. With real data I suppose your node sets might not be equivalent like here and you probably will see differences there too.
On the path lengths I'm not quite sure how you would go about that. The intersection just checks which nodes and edges are shared between two graphs and returns those that are in both, unaware of any other conditions I suspect. There might be a way to impose some additional constraints by adapting the source code with of the intersection function with some conditional checks.
I guess this doesn't check the number of paths but rather the number of edges, so I suppose you're looking for something more specific than this. But at the very least no path can exist outside of the intersection, since all shared paths must contain the same edges in both (since if an edge is missing from a path in either, it cannot exist as a path in the shared solution).
Hope this helps in some way shape or form, though I feel I've oversimplified your question quite a bit.
EDIT: Intuitively, the full solution to your question might be to simply enumerate all possible paths in the intersection.
I am doing a college project where I need to compare a string with list of other strings. I want to know if we have any kind of library which can do this or not.
Suppose I have a table called : DOCTORS_DETAILS
Other Table names are : HOSPITAL_DEPARTMENTS , DOCTOR_APPOINTMENTS, PATIENT_DETAILS,PAYMENTS etc.
Now I want to calculate which one among those are more relevant to DOCTOR_DETAILS ?
Expected output can be,
DOCTOR_APPOINTMENTS - More relevant because of the term doctor matches in both string
PATIENT_DETAILS - The term DETAILS present in both string
HOSPITAL_DEPARTMENTS - least relevant
PAYMENTS - least relevant
Therefore I want to find RELEVENCE based on number of similar terms present on both the strings in question.
Ex : DOCTOR_DETAILS -> DOCTOR_APPOITMENT(1/2) > DOCTOR_ADDRESS_INFORMATION(1/3) > DOCTOR_SPECILIZATION_DEGREE_INFORMATION (1/4) > PATIENT_INFO (0/2)
Semantic similarity is a common NLP problem. There are multiple approaches to look into, but at their core they all are going to boil down to:
Turn each piece of text into a vector
Measure distance between vectors, and call closer vectors more similar
Three possible ways to do step 1 are:
tf-idf
fasttext
bert-as-service
To do step 2, you almost certainly want to use cosine distance. It is pretty straightforward with Python, here is a implementation from a blog post:
import numpy as np
def cos_sim(a, b):
"""Takes 2 vectors a, b and returns the cosine similarity according
to the definition of the dot product
"""
dot_product = np.dot(a, b)
norm_a = np.linalg.norm(a)
norm_b = np.linalg.norm(b)
return dot_product / (norm_a * norm_b)
For your particular use case, my instincts say to use fasttext. So, the official site shows how to download some pretrained word vectors, but you will want to download a pretrained model (see this GH issue, use https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M-subword.bin.zip),
Then you'd then want to do something like:
import fasttext
model = fasttext.load_model("model_filename.bin")
def order_tables_by_name_similarity(main_table, candidate_tables):
'''Note: we use a fasttext model, not just pretrained vectors, so we get subword information
you can modify this to also output the distances if you need them
'''
main_v = model[main_table]
similarity_to_main = lambda w: cos_sim(main_v, model[w])
return sorted(candidate_tables, key=similarity_to_main, reverse=True)
order_tables_by_name_similarity("DOCTORS_DETAILS", ["HOSPITAL_DEPARTMENTS", "DOCTOR_APPOINTMENTS", "PATIENT_DETAILS", "PAYMENTS"])
# outputs: ['PATIENT_DETAILS', 'DOCTOR_APPOINTMENTS', 'HOSPITAL_DEPARTMENTS', 'PAYMENTS']
If you need to put this in production, the giant model size (6.7GB) might be an issue. At that point, you'd want to build your own model, and constrain the model size. You can probably get roughly the same accuracy out of a 6MB model!
Recently, I am implementing an algorithm from a paper that I will be using in my master's work, but I've come across some problems regarding the time it is taking to perform some operations.
Before I get into details, I just want to add that my data set comprehends roughly 4kk entries of data points.
I have two lists of tuples that I've get from a framework (annoy) that calculates cosine similarity between a vector and every other vector in the dataset. The final format is like this:
[(name1, cosine), (name2, cosine), ...]
Because of the algorithm, I have two of that lists with the same names (first value of the tuple) in it, but two different cosine similarities. What I have to do is to sum the cosines from both lists, and then order the array and get the top-N highest cosine values.
My issue is: is taking too long. My actual code for this implementation is as following:
def topN(self, user, session):
upref = self.m2vTN.get_user_preference(user)
spref = self.sm2vTN.get_user_preference(session)
# list of tuples 1
most_su = self.indexer.most_similar(upref, len(self.m2v.wv.vocab))
# list of tuples 2
most_ss = self.indexer.most_similar(spref, len(self.m2v.wv.vocab))
# concat both lists and add into a dict
d = defaultdict(int)
for l, v in (most_ss + most_su):
d[l] += v
# convert the dict into a list, and then sort it
_list = list(d.items())
_list.sort(key=lambda x: x[1], reverse=True)
return [x[0] for x in _list[:self.N]]
How do I make this code faster? I've tried using threads but I'm not sure if it will make it faster. Getting the lists is not the problem here, but the concatenation and sorting is.
Thanks! English is not my native language, so sorry for any misspelling.
What do you mean by "too long"? How large are the two lists? Is there a chance your model, and interim results, are larger than RAM and thus forcing virtual-memory paging (which would create frustrating slowness)?
If you are in fact getting the cosine-similarity with all vectors in the model, the annoy-indexer isn't helping any. (Its purpose is to get a small subset of nearest-neighbors much faster, at the expense of perfect accuracy. But if you're calculating the similarity to every candidate, there's no speedup or advantage to using ANNOY.
Further, if you're going to combine all of the distances from two such calculation, there's no need for the sorting that most_similar() usually does - it just makes combining the values more complex later. For the gensim vector-models, you can supply a False-ish topn value to just get the unsorted distances to all model vectors, in order. Then you'd have two large arrays of the distances, in the model's same native order, which are easy to add together elementwise. For example:
udists = self.m2v.most_similar(positive=[upref], topn=False)
sdists = self.m2v.most_similar(positive=[spref], topn=False)
combined_dists = udists + sdists
The combined_dists aren't labeled, but will be in the same order as self.m2v.index2entity. You could then sort them, in a manner similar to what the most_similar() method itself does, to find the ranked closest. See for example the gensim source code for that part of most_similar():
https://github.com/RaRe-Technologies/gensim/blob/9819ce828b9ed7952f5d96cbb12fd06bbf5de3a3/gensim/models/keyedvectors.py#L557
Finally, you might not need to be doing this calculation yourself at all. You can provide more-than-one vector to most_similar() as the positive target, and then it will return the vectors closest to the average of both vectors. For example:
sims = self.m2v.most_similar(positive=[upref, spref], topn=len(self.m2v))
This won't be the same value/ranking as your other sum, but may behave very similarly. (If you wanted less-than-all of the similarities, then it might make sense to use the ANNOY indexer this way, as well.)
I am trying to implement relation extraction between verb pairs. I want to use dependency path from one verb to the other as a feature for my classifier (predicts if relation X exists or not). But I am not sure how to encode the dependency path as a feature. Following are some example dependency paths, as space separated relation annotations from StanfordCoreNLP Collapsed Dependencies:
nsubj acl nmod:from acl nmod:by conj:and
nsubj nmod:into
nsubj acl:relcl advmod nmod:of
It is important to keep in mind that these path are of variable length and a relation could reappear without any restriction.
Two compromising ways of encoding this feature that come to my mind are:
1) Ignore the sequence, and just have one feature for each relation with its value being the number of times it appears in the path
2) Have a sliding window of length n, and have one feature for each possible pair of relations with the value being the number of times those two relations appeared consecutively. I suppose this is how one encodes n-grams. However, the number of possible relations is 50, which means I cannot really go with this approach.
Any suggestions are welcomed.
We had a project that built a classifier based off of dependency paths. I asked the group member who developed the system, and he said:
indicator feature for the whole path
So if you have the training data point (verb1 -e1-> w1 -e2-> w2 -e3-> w3 -e4-> verb2, relation1) the feature would be (e1-e2-e3-e4)
And he also did ngram sequences, so for that same data point, you would also have (e1), (e2), (e3), (e4), (e1-e2), (e2-e3), (e3-e4), (e1-e2-e3), (e2-e3-e4)
He also recommended collapsing appositive edges to make the paths smaller.
Also, I should note that he developed a set of high precision rules for each relation, and used this to create a large set of training data.
If you compare two sets of data (such as two files), the differences between these sets can be displayed in two columns, or two panes, such as WinMerge does.
But are there any visual paradigms to display the differences between multiple data sets?
Update
The starting point of my question was the assumption that displaying differences between 2 files is relatively easy, as I mentioned WinMerge, whereas comparing 3 or more text files turns out to be more complicated, as there will be more and more differences between, say, different versions of a document that have been created over time.
How would you highlight parts of the file that are the same in 2 versions, but different from other versions?
The data sets I have in mind are objects (A, B, C, ...) which may or may not exist and have properties (a, b, c, ...) which may be set or not set.
Example:
Set 1: A(a, b, c), B(b, c), C(c)
Set 2: A(a, b, c), B(b), C(c)
Set 3: A(a, b), B(b)
If you compare 2 sets, e.g. 1 and 2, the difference would be in B(c). Comparing sets 2 and 3 results in the difference A(c) and C().
If you compare all 3 sets, you end up with 3 comparisons (n * (n-1) / 2)
I have a different view than some of those who provided Answers--i.e., that you need to further specify the problem. The abstraction level is about right. Further specification would make the problem easier, but the solution less useful.
A couple of years ago, i saw a graphic on ProgrammableWeb--it compared the results from a search on Yahoo with the results from the same search on Google. There's a lot of information to covey: some results are in both sets, some in just one, and the common results will have different positions in the respective engine's results, which somehow has to be shown.
I like the graphic and reimplemented it in Matplotlib (a Python scientific plotting library). Below is an example using some random points as well as python code i used to generate it:
from matplotlib import pyplot as PLT
xvals = NP.array([(2,3), (5,7), (8,6), (1.5,1.8), (3.0,3.8), (5.3,5.2),
(3.7,4.1), (2.9, 3.7), (8.4, 6.1), (7.1, 6.4)])
yvals = NP.tile( NP.array([5,3]), [10,1] )
fig = PLT.figure()
ax1 = fig.add_subplot(111)
ax1.plot(x, y, "-", lw=3, color='b')
ax1.plot(x, y2, "-", lw=3, color='b')
for a, b in zip(xvals, yvals) : ax1.plot(a,b,'-o',ms=8,mfc='orange', color='g')
PLT.axis("off")
PLT.show()
This model has some interesting features: (i) it actually deals with 'similarity' on a per-item basis (the vertically-oriented line connecting the dots) rather than aggregate similarity; (ii) the degree of similarity between two data points is proportional to the angle of the line connecting them--90 degrees if they are equal, with a decreasing angle as the difference increases; this is very intuitive; (iii) cases in which a point in one data set is not present in the second data set are easy to show--a point will appear on one of the two lines but without a line connecting it to a point on the other line.
This model works well for comparing search results because each search result has a 'score' (its index, or order in the Results List). For other types of data, you might have to assign a score to each data point--a similarity metric might i suppose (in a sense, that's actually what the search result order is, an distance from the top of the list)
Since there has been so much work into displaying a diff of two files, you might start by expressing your 'multiple data sets' in an appropriate text format, then using whatever you want to show a diff between those text formats.
But you should tell us more about your data sets!
I experimented a bit, and implemented two displays:
Matrix
Timeline
I agree with Peter, you should specify what type your data is and what you wish to bring out in the comparison.
Depending on the nature of the data/comparison you can consider different visualisations. Is your data ordered or unordered? How many things are you comparing, i.e. fine grain or gross comparison?
Examples:
Visualizing a comparison of unordered data could just be plotting the two histograms of your sets (i.e. distributions):
image source
On the other hand, comparing a huge ordered dataset like DNA can be done innovatively.
Also, check out visual complexity, it's a great resource for interesting visualization.