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.
Related
Description of the problem
My goal is quite basic: to plot time series in an interactive plot. After some research I decided to give a try to Altair.
There are already QGIS plugins for time-series visualisation, but as far as I'm aware, none for plotting time-series at vector-level, interactively clicking on a map and selecting a Polygon. So that's why I decided to go for a self-made solution using Altair, maybe combining it with Folium to add functionalities later on.
I'm totally new to the Altair library (as well as Vega and Vega-lite), and quite new in datascience and data visualisation as well... so apologies in advance for my ignorance!
There are already well explained tutorials on how to plot time series with Altair (for example here, or in the official website). However, my study case has some particularities that, as far as I've seen, have not yet been approached altogether.
The data is produced using the Python API for Google Earth Engine and preprocessed with Python and the pandas/geopandas libraries:
In Google Earth Engine, a vegetation index (NDVI in the current case) is computed at pixel-level for a certain region of interest (ROI). Then the function image.reduceRegions() is mapped across the ImageCollection to compute the mean of the ndvi in every polygon of a FeatureCollection element, which represent agricultural parcels. The resulting vector file is exported.
Under a Jupyter-lab environment, the data is loaded into a geopandas GeoDataFrame object and preprocessed, transposing the DataFrame and creating a datetime column, among others, in order to have the data well-shaped for time-series representation with Altair.
Data overview after preprocessing:
My "final" goal would be to show, in the same graphic, an interactive line plot with a set of lines representing each one an agricultural parcel, with parcels categorized by crop types in different colours, e.g. corn in green, wheat in yellow, peer trees in brown... (the information containing the crop type of each parcel can be added to the DataFrame making a join with another DataFrame).
I am thinking of something looking more or less like the following example, with legend's years being the parcels coloured by crop types:
But so far I haven't managed to make my data look this way... at all.
As you can see there are many nulls in the data (this is due to the application of a cloud masking function and to the fact that there are several Sentinel-2 orbits intersecting the ROI). I would like to just omit the non-null values for earch column/parcel, but I don't know if this data configuration can pose problems (any advice on that?).
So far I got:
The generation of the preceding graphic, for a single parcel, takes already around 23 seconds. Which is something maybe shoud/cloud be improved (how?)
And more importantly, the expected line representing the item/polygon/parcel's values (NDVI) is not even shown in the plot (note that I chose a parcel containing rather few non-null values).
For sure I am doing many things wrong. Would be great to get some advice to solve (some of) them.
Sample of the data and code to reproduce the issue
Here's a text sample of the data in JSON format, and the code used to reproduce the issue is the following:
import pandas as pd
import geopandas as gpd
import altair as alt
df= pd.read_json(r"path\to\json\file.json")
df['date']= pd.to_datetime(df['date'])
print(gdf.dtypes)
df
Output:
lines=alt.Chart(df).mark_line().encode(
x='date:O',
y='17811:Q',
color=alt.Color(
'17811:Q', scale=alt.Scale(scheme='redyellowgreen', domain=(-1, 1)))
)
lines.properties(width=700, height=600).interactive()
Output:
Thanks in advance for your help!
If I understand correctly, it is mostly the format of your dataframe that needs to be changed from wide to long, which you can do either via .melt in pandas or .transform_fold in Altair. With melt, the default names are 'variable' (the previous columns name) and 'value' (the value for each column) for the melted columns:
alt.Chart(df.melt(id_vars='date'), width=500).mark_line().encode(
x='date:T',
y='value',
color=alt.Color('variable')
)
The gaps comes from the NaNs; if you want Altair to interpolate missing values, you can drop the NaNs:
alt.Chart(df.melt(id_vars='date').dropna(), width=500).mark_line().encode(
x='date:T',
y='value',
color=alt.Color('variable')
)
If you want to do it all in Altair, the following is equivalent to the last pandas example above (the transform uses 'key' instead of 'variable' as the name for the former columns). I also use and ordinal instead of nominal type for the color encoding to show how to make the colors more similar to your example.:
alt.Chart(df, width=500).mark_line().encode(
x='date:T',
y='value:Q',
color=alt.Color('key:O')
).transform_fold(
df.drop(columns='date').columns.tolist()
).transform_filter(
'isValid(datum.value)'
)
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.
first time user of this forum - guidance on how to provide enough information is very appreciated. I am trying to replicate the presentation of data used in the Medical education field. This will help improve the quality of examiners' marking of trainees in a Clinical Exam. What I would like to communicate will be similar to what is already communicated in the College of General Practitioners regarding one of their own exams, please see www.gp10.com.au/slides/thursday/slide29.pdf to help understand what it is I want to present. I have access to Excel, SPSS and R, so any help with any of these would be great. However as a first attempt I have used SPSS and created 3 variables: dummy variable, a "station score" and a "global rating score"(GRS). The "station score"(ST) is a value between 0 and 10 (non-integers) and is on the y-axis similar to the pdf presentation of "Candidate Final Marks". The x-axis is the "global rating scale", an integer from 1 to 6 and is represented in the pdf as the "Overall Performance Scale". When I use SPSS's boxplot I get a boxplot as depicted.
.
What I would like to do is overlay a single examiners own scoring of X number of examinees. So for one examiner (examiner A) provided the following marks:
ST: 5.53,7.38,7.38,7.44,6.81
GRS: 3,4,4,5,3
(this is transposed into two columns).
Whether it be SPSS, Excel or R how would I be able to overlay the box and whisker plots with the individual data points provided by the one examiner? This would help show the degree to which the examiners' marking styles are in concordance with the expected distribution of ST scores across GRS. Any help greatly appreciated! I like Excel graphics but I have found it very difficult to work with when choosing the examiners' data as a separate series - somehow the examiners' GRS scores do not line up nicely on the x-axis. I am very new to R but am also very interested in R, and would expend time to get a good result in R if a good result is viable. I understand JMP may be preferable for this type of thing but access to this may not be possible.