My question is: is there any algorithm that can convert a SMILES structure into a topological fingerprint? For example if glycerol is the input the answer would be 3 x -OH , 2x -CH2 and 1x -CH.
I'm trying to build a python script that can predict the density of a mixture using an artificial neural network. As an input I want to have the structure/fingerprint of my molecules starting from the SMILES structure.
I'm already familiar with -rdkit and the morganfingerprint but that is not what i'm looking for. I'm also aware that I can use the 'matching substructure' search in rdkit, but then I would have to define all the different subgroups. Is there any more convenient/shorter way?
For most of the structures, there's no existing option to find the fragments. However, there's a module in rdkit that can provide you the number of fragments especially when it's a function group. Check it out here. As an example, let's say you want to find the number of aliphatic -OH groups in your molecule. You can simply call the following function to do that
from rdkit.Chem.Fragments import fr_Al_OH
fr_Al_OH(mol)
or the following would return the number of aromatic -OH groups:
from rdkit.Chem.Fragments import fr_Ar_OH
fr_Ar_OH(mol)
Similarly, there are 83 more functions available. Some of them would be useful for your task. For the ones, you don't get the pre-written function, you can always go to the source code of these rdkit modules, figure out how they did it, and then implement them for your features. But as you already mentioned, the way would be to define a SMARTS string and then fragment matching. The fragment matching module can be found here.
If you want to predict densities of pure components before predicting the mixtures I recommend the following paper:
https://pubs.acs.org/doi/abs/10.1021/acs.iecr.6b03809
You can use the fragments specified by rdkit as mnis proposes. Or you could specify the groups as SMARTS patterns and look for them yourself using GetSubstructMatches as you proposed yourself.
Dissecting a molecule into specific groups is not as straightforward as it might appear in the first place. You could also use an algorithm I published a while ago:
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0382-3
It includes a list of SMARTS for the UNIFAC model, but you could also use them for other things, like density prediction.
Related
I'm a bit confused over the difference between docstrings and comments in python.
In my class my teacher introduced something known as a 'design recipe', a set of steps that will supposedly help us students plot and organize our coding better in Python. From what I understand, the below is an example of the steps we follow - this so call design recipe (the stuff in the quotations):
def term_work_mark(a0_mark, a1_mark, a2_mark, ex_mark, midterm_mark):
''' (float, float, float, float, float) -> float
Takes your marks on a0_mark, a1_mark, a2_mark, ex_mark and midterm_mark,
calculates their respective weight contributions and sums these
contributions to deliver your overall term mark out of a maximum of 55 (This
is because the exam mark is not taken account of in this function)
>>>term_work_mark(5, 5, 5, 5, 5)
11.8
>>>term_work_mark(0, 0, 0, 0, 0)
0.0
'''
a0_component = contribution(a0_mark, a0_max_mark, a0_weight)
a1_component = contribution(a1_mark, a1_max_mark, a1_weight)
a2_component = contribution(a2_mark, a2_max_mark, a2_weight)
ex_component = contribution(ex_mark, exercises_max_mark,exercises_weight)
mid_component = contribution(midterm_mark, midterm_max_mark, midterm_weight)
return (a0_component + a1_component + a2_component + ex_component +
mid_component)
As far as I understand this is basically a docstring, and in our version of a docstring it must include three things: a description, examples of what your function should do if you enter it in the python shell, and a 'type contract', a section that shows you what types you enter and what types the function will return.
Now this is all good and done, but our assignments require us to also have comments which explain the nature of our functions, using the token '#' symbol.
So, my question is, haven't I already explained what my function will do in the description section of the docstring? What's the point of adding comments if I'll essentially be telling the reader the exact same thing?
It appears your teacher is a fan of How to Design Programs ;)
I'd tackle this as writing for two different audiences who won't always overlap.
First there are the docstrings; these are for people who are going to be using your code without needing or wanting to know how it works. Docstrings can be turned into actual documentation. Consider the official Python documentation - What's available in each library and how to use it, no implementation details (Unless they directly relate to use)
Secondly there are in-code comments; these are to explain what is going on to people (generally you!) who want to extend the code. These will not normally be turned into documentation as they are really about the code itself rather than usage. Now there are about as many opinions on what makes for good comments (or lack thereof) as there are programmers. My personal rules of thumb for adding comments are to explain:
Parts of the code that are necessarily complex. (Optimisation comes to mind)
Workarounds for code you don't have control over, that may otherwise appear illogical
I'll admit to TODOs as well, though I try to keep that to a minimum
Where I've made a choice of a simpler algorithm where a better performing (but more complex) option can go if performance in that section later becomes critical
Since you're coding in an academic setting, and it sounds like your lecturer is going for verbose, I'd say just roll with it. Use code comments to explain how you are doing what you say you are doing in the design recipe.
I believe that it's worth to mention what PEP8 says, I mean, the pure concept.
Docstrings
Conventions for writing good documentation strings (a.k.a. "docstrings") are immortalized in PEP 257.
Write docstrings for all public modules, functions, classes, and methods. Docstrings are not necessary for non-public methods, but you should have a comment that describes what the method does. This comment should appear after the def line.
PEP 257 describes good docstring conventions. Note that most importantly, the """ that ends a multiline docstring should be on a line by itself, e.g.:
"""Return a foobang
Optional plotz says to frobnicate the bizbaz first.
"""
For one liner docstrings, please keep the closing """ on the same line.
Comments
Block comments
Generally apply to some (or all) code that follows them, and are indented to the same level as that code. Each line of a block comment starts with a # and a single space (unless it is indented text inside the comment).
Paragraphs inside a block comment are separated by a line containing a single #.
Inline Comments
Use inline comments sparingly.
An inline comment is a comment on the same line as a statement. Inline comments should be separated by at least two spaces from the statement. They should start with a # and a single space.
Inline comments are unnecessary and in fact distracting if they state the obvious.
Don't do this:
x = x + 1 # Increment x
But sometimes, this is useful:
x = x + 1 # Compensate for border
Reference
https://www.python.org/dev/peps/pep-0008/#documentation-strings
https://www.python.org/dev/peps/pep-0008/#inline-comments
https://www.python.org/dev/peps/pep-0008/#block-comments
https://www.python.org/dev/peps/pep-0257/
First of all, for formatting your posts you can use the help options above the text area you type your post.
And about comments and doc strings, the doc string is there to explain the overall use and basic information of the methods. On the other hand comments are meant to give specific information on blocks or lines, #TODO is used to remind you what you want to do in future, definition of variables and so on. By the way, in IDLE the doc string is shown as a tool tip when you hover over the method's name.
Quoting from this page http://www.pythonforbeginners.com/basics/python-docstrings/
Python documentation strings (or docstrings) provide a convenient way
of associating documentation with Python modules, functions, classes,
and methods.
An object's docsting is defined by including a string constant as the
first statement in the object's definition.
It's specified in source code that is used, like a comment, to
document a specific segment of code.
Unlike conventional source code comments the docstring should describe
what the function does, not how.
All functions should have a docstring
This allows the program to inspect these comments at run time, for
instance as an interactive help system, or as metadata.
Docstrings can be accessed by the __doc__ attribute on objects.
Docstrings can be accessed through a program (__doc__) where as inline comments cannot be accessed.
Interactive help systems like in bpython and IPython can use docstrings to display the docsting during the development. So that you dont have to visit the program everytime.
After re-reading the off/on topic lists, I'm still not certain if this question is best posted to this site, so apologies in advance, if it is not.
Overview:
I am working on a project that mixes several programming languages and we are trying to determine important considerations for the command used to call one in particular.
For definiteness, I will list the specific languages; however, I think the principles ought to be general, so familiarity with these specific languages is not really essential.
Specific Context
Specifically, we are using: Maxima, KaTeX, Markdown and HTML). While building the prototype, we have used the following (I believe, standard) conventions:
KaTeX delimited by $ $ or $$ $$;
HTML delimited by < > </ > pairs;
Markdown works anywhere in the body, except within KaTeX or Maxima environments;
The only non-standard convention we used during this design phase was to call on Maxima using \comp{<Maxima commands>}. This command works within all the other environments (which is desired).
Now that we are ready to start using the platform, it has become apparent that this temporary command for calling Maxima is cumbersome for our users. The vast majority of use cases involve simply calling a single variable or function, e.g.
As such, we have $\eval{function-name()}(\eval{variable-name})$
as opposed to actually using Maxima for computation, e.g.
Here, it is clear that $\eval{a} + \eval{b} = \eval{a+b}$
(where \eval{a+b} would return the actual sum, as calculated by Maxima).
As such, our users would prefer a delimiter-less command option for invoking a single variable or function, e.g. \#<variable-name-in-Maxima> and \#<function-name>(<argument>) (where # is some reserved character not used in the other languages), while also having a delimited alternative for the (much less frequent) cases where they actually want to use Maxima for computation; perhaps something like \#{a+b}.
However, we have a general sense that this is not a best practice, even though we can't foresee any specific issue.
"Research" / Comparisons:
Indeed, there is precedence for delimit-less expressions for single arguments like x^2 (on any calculator) or Knuth's a \over b in TeX (which persists in LaTeX with \frac12 being parsed as \frac{1}{2}.
IIRC Knuth's point was that this delimit-less notation was more semantic (and so, in his view, preferable), and because delimiters can be added, ambiguity can be avoided, whenever the need arises: e.g. x^{22}, {a+b}\over{c+d} and \frac{12}{3}.
The Question, Proper:
Can anyone point to or explain actual shortcomings / risks associated with a dual solution like:
\#<var>, \#<function>(<arg>) and,
\#[<extended expression>],
(where # is a reserved (& escapable) character), for calling one language amongst others, as opposed to only using a delimited command?
Any alternative suggestions for how to achieve the ease-of-use and more semantic code enabled by the above solution, while keeping the code unambiguous would be very much welcome and appreciated.
I read quite a lot about the visitor pattern and its supposed advantages. To me however it seems they are not that much advantages when applied in practice:
"Convenient" and "elegant" seems to mean lots and lots of boilerplate code
Therefore, the code is hard to follow. Also 'accept'/'visit' is not very descriptive
Even uglier boilerplate code if your programming language has no method overloading (i.e. Vala)
You cannot in general add new operations to an existing type hierarchy without modification of all classes, since you need new 'accept'/'visit' methods everywhere as soon as you need an operation with different parameters and/or return value (changes to classes all over the place is one thing this design pattern was supposed to avoid!?)
Adding a new type to the type hierarchy requires changes to all visitors. Also, your visitors cannot simply ignore a type - you need to create an empty visit method (boilerplate again)
It all just seems to be an awful lot of work when all you want to do is actually:
// Pseudocode
int SomeOperation(ISomeAbstractThing obj) {
switch (type of obj) {
case Foo: // do Foo-specific stuff here
case Bar: // do Bar-specific stuff here
case Baz: // do Baz-specific stuff here
default: return 0; // do some sensible default if type unknown or if we don't care
}
}
The only real advantage I see (which btw i haven't seen mentioned anywhere): The visitor pattern is probably the fastest method to implement the above code snippet in terms of cpu time (if you don't have a language with double dispatch or efficient type comparison in the fashion of the pseudocode above).
Questions:
So, what advantages of the visitor pattern have I missed?
What alternative concepts/data structures could be used to make the above fictional code sample run equally fast?
For as far as I have seen so far there are two uses / benefits for the visitor design pattern:
Double dispatch
Separate data structures from the operations on them
Double dispatch
Let's say you have a Vehicle class and a VehicleWasher class. The VehicleWasher has a Wash(Vehicle) method:
VehicleWasher
Wash(Vehicle)
Vehicle
Additionally we also have specific vehicles like a car and in the future we'll also have other specific vehicles. For this we have a Car class but also a specific CarWasher class that has an operation specific to washing cars (pseudo code):
CarWasher : VehicleWasher
Wash(Car)
Car : Vehicle
Then consider the following client code to wash a specific vehicle (notice that x and washer are declared using their base type because the instances might be dynamically created based on user input or external configuration values; in this example they are simply created with a new operator though):
Vehicle x = new Car();
VehicleWasher washer = new CarWasher();
washer.Wash(x);
Many languages use single dispatch to call the appropriate function. Single dispatch means that during runtime only a single value is taken into account when determining which method to call. Therefore only the actual type of washer we'll be considered. The actual type of x isn't taken into account. The last line of code will therefore invoke CarWasher.Wash(Vehicle) and NOT CarWasher.Wash(Car).
If you use a language that does not support multiple dispatch and you do need it (I can honoustly say I have never encountered such a situation though) then you can use the visitor design pattern to enable this. For this two things need to be done. First of all add an Accept method to the Vehicle class (the visitee) that accepts a VehicleWasher as a visitor and then call its operation Wash:
Accept(VehicleWasher washer)
washer.Wash(this);
The second thing is to modify the calling code and replace the washer.Wash(x); line with the following:
x.Accept(washer);
Now for the call to the Accept method the actual type of x is considered (and only that of x since we are assuming to be using a single dispatch language). In the implementation of the Accept method the Wash method is called on the washer object (the visitor). For this the actual type of the washer is considered and this will invoke CarWasher.Wash(Car). By combining two single dispatches a double dispatch is implemented.
Now to eleborate on your remark of the terms like Accept and Visit and Visitor being very unspecific. That is absolutely true. But it is for a reason.
Consider the requirement in this example to implement a new class that is able to repair vehicles: a VehicleRepairer. This class can only be used as a visitor in this example if it would inherit from VehicleWasher and have its repair logic inside a Wash method. But that ofcourse doesn't make any sense and would be confusing. So I totally agree that design patterns tend to have very vague and unspecific naming but it does make them applicable to many situations. The more specific your naming is, the more restrictive it can be.
Your switch statement only considers one type which is actually a manual way of single dispatch. Applying the visitor design pattern in the above way will provide double dispatch.
This way you do not necessarily need additional Visit methods when adding additional types to your hierarchy. Ofcourse it does add some complexity as it makes the code less readable. But ofcourse all patterns come at a price.
Ofcourse this pattern cannot always be used. If you expect lots of complex operations with multiple parameters then this will not be a good option.
An alternative is to use a language that does support multiple dispatch. For instance .NET did not support it until version 4.0 which introduced the dynamic keyword. Then in C# you can do the following:
washer.Wash((dynamic)x);
Because x is then converted to a dynamic type its actual type will be considered for the dispatch and so both x and washer will be used to select the correct method so that CarWasher.Wash(Car) will be called (making the code work correctly and staying intuitive).
Separate data structures and operations
The other benefit and requirement is that it can separate the data structures from the operations. This can be an advantage because it allows new visitors to be added that have there own operations while it also allows data structures to be added that 'inherit' these operations. It can however be only applied if this seperation can be done / makes sense. The classes that perform the operations (the visitors) do not know the structure of the data structures nor do they have to know that which makes code more maintainable and reusable. When applied for this reason the visitors have operations for the different elements in the data structures.
Say you have different data structures and they all consist of elements of class Item. The structures can be lists, stacks, trees, queues etc.
You can then implement visitors that in this case will have the following method:
Visit(Item)
The data structures need to accept visitors and then call the Visit method for each Item.
This way you can implement all kinds of visitors and you can still add new data structures as long as they consist of elements of type Item.
For more specific data structures with additional elements (e.g. a Node) you might consider a specific visitor (NodeVisitor) that inherits from your conventional Visitor and have your new data structures accept that visitor (Accept(NodeVisitor)). The new visitors can be used for the new data structures but also for the old data structures due to inheritence and so you do not need to modify your existing 'interface' (the super class in this case).
In my personal opinion, the visitor pattern is only useful if the interface you want implemented is rather static and doesn't change a lot, while you want to give anyone a chance to implement their own functionality.
Note that you can avoid changing everything every time you add a new method by creating a new interface instead of modifying the old one - then you just have to have some logic handling the case when the visitor doesn't implement all the interfaces.
Basically, the benefit is that it allows you to choose the correct method to call at runtime, rather than at compile time - and the available methods are actually extensible.
For more info, have a look at this article - http://rgomes-info.blogspot.co.uk/2013/01/a-better-implementation-of-visitor.html
By experience, I would say that "Adding a new type to the type hierarchy requires changes to all visitors" is an advantage. Because it definitely forces you to consider the new type added in ALL places where you did some type-specific stuff. It prevents you from forgetting one....
This is an old question but i would like to answer.
The visitor pattern is useful mostly when you have a composite pattern in place in which you build a tree of objects and such tree arrangement is unpredictable.
Type checking may be one thing that a visitor can do, but say you want to build an expression based on a tree that can vary its form according to a user input or something like that, a visitor would be an effective way for you to validate the tree, or build a complex object according to the items found on the tree.
The visitor may also carry an object that does something on each node it may find on that tree. this visitor may be a composite itself chaining lots of operations on each node, or it can carry a mediator object to mediate operations or dispatch events on each node.
You imagination is the limit of all this. you can filter a collection, build an abstract syntax tree out of an complete tree, parse a string, validate a collection of things, etc.
http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/comment-page-1/#comment-73511
I am trying to understand NLTK using this link. I cannot understand how the values of feature_probdist and show_most_informative_features are computed.
Esp when the word "best" does not come how the likelihood is computed as 0.077 . I was trying since long back
That is because it is explaining code from NLTK's source code but not displaying all of it. The full code is available on NLTK's website (and is also linked to in the article you referenced). These are a field within a method and a method (respectively) of the NaiveBayesClassifier class within NLTK. This class is of course using a Naive Bayes classifier, which is essentially a modification of Bayes Theorum with a strong (naive) assumption that each event is independent.
feature_probdist = "P(fname=fval|label), the probability distribution for feature values, given labels. It is expressed as a dictionary whose keys are (label,fname) pairs and whose values are ProbDistIs over feature values. I.e., P(fname=fval|label) = feature_probdist[label,fname].prob(fval). If a given (label,fname) is not a key in feature_probdist, then it is assumed that the corresponding P(fname=fval|label) is 0 for all values of fval."
most_informative features returns "a list of the 'most informative' features used by this classifier. For the purpose of this function, the informativeness of a feature (fname,fval) is equal to the highest value of P(fname=fval|label), for any label, divided by the lowest value of P(fname=fval|label), for any label:"
max[ P(fname=fval|label1) / P(fname=fval|label2) ]
Check out the source code for the entire class if this is still unclear, the article's intent was not to break down how NLTK works under the hood in depth, but rather just to give a basic concept of how to use it.
I want to build a bot that asks someone a few simple questions and branches based on the answer. I realize parsing meaning from the human responses will be challenging, but how do you setup the program to deal with the "state" of the conversation?
It will be a one-to-one conversation between a human and the bot.
You probably want to look into Markov Chains as the basics for the bot AI. I wrote something a long time ago (the code to which I'm not proud of at all, and needs some mods to run on Python > 1.5) that may be a useful starting place for you: http://sourceforge.net/projects/benzo/
EDIT: Here's a minimal example in Python of a Markov Chain that accepts input from stdin and outputs text based on the probabilities of words succeeding one another in the input. It's optimized for IRC-style chat logs, but running any decent-sized text through it should demonstrate the concepts:
import random, sys
NONWORD = "\n"
STARTKEY = NONWORD, NONWORD
MAXGEN=1000
class MarkovChainer(object):
def __init__(self):
self.state = dict()
def input(self, input):
word1, word2 = STARTKEY
for word3 in input.split():
self.state.setdefault((word1, word2), list()).append(word3)
word1, word2 = word2, word3
self.state.setdefault((word1, word2), list()).append(NONWORD)
def output(self):
output = list()
word1, word2 = STARTKEY
for i in range(MAXGEN):
word3 = random.choice(self.state[(word1,word2)])
if word3 == NONWORD: break
output.append(word3)
word1, word2 = word2, word3
return " ".join(output)
if __name__ == "__main__":
c = MarkovChainer()
c.input(sys.stdin.read())
print c.output()
It's pretty easy from here to plug in persistence and an IRC library and have the basis of the type of bot you're talking about.
Folks have mentioned already that statefulness isn't a big component of typical chatbots:
a pure Markov implementations may express a very loose sort of state if it is growing its lexicon and table in real time—earlier utterances by the human interlocutor may get regurgitated by chance later in the conversation—but the Markov model doesn't have any inherent mechanism for selecting or producing such responses.
a parsing-based bot (e.g. ELIZA) generally attempts to respond to (some of the) semantic content of the most recent input from the user without significant regard for prior exchanges.
That said, you certainly can add some amount of state to a chatbot, regardless of the input-parsing and statement-synthesis model you're using. How to do that depends a lot on what you want to accomplish with your statefulness, and that's not really clear from your question. A couple general ideas, however:
Create a keyword stack. As your human offers input, parse out keywords from their statements/questions and throw those keywords onto a stack of some sort. When your chatbot fails to come up with something compelling to respond to in the most recent input—or, perhaps, just at random, to mix things up—go back to your stack, grab a previous keyword, and use that to seed your next synthesis. For bonus points, have the bot explicitly acknowledge that it's going back to a previous subject, e.g. "Wait, HUMAN, earlier you mentioned foo. [Sentence seeded by foo]".
Build RPG-like dialogue logic into the bot. As your parsing human input, toggle flags for specific conversational prompts or content from the user and conditionally alter what the chatbot can talk about, or how it communicates. For example, a chatbot bristling (or scolding, or laughing) at foul language is fairly common; a chatbot that will get het up, and conditionally remain so until apologized to, would be an interesting stateful variation on this. Switch output to ALL CAPS, throw in confrontational rhetoric or demands or sobbing, etc.
Can you clarify a little what you want the state to help you accomplish?
Imagine a neural network with parsing capabilities in each node or neuron. Depending on rules and parsing results, neurons fire. If certain neurons fire, you get a good idea about topic and semantic of the question and therefore can give a good answer.
Memory is done by keeping topics talked about in a session, adding to the firing for the next question, and therefore guiding the selection process of possible answers at the end.
Keep your rules and patterns in a knowledge base, but compile them into memory at start time, with a neuron per rule. You can engineer synapses using something like listeners or event functions.
I think you can look at the code for Kooky, and IIRC it also uses Markov Chains.
Also check out the kooky quotes, they were featured on Coding Horror not long ago and some are hilarious.
I think to start this project, it would be good to have a database with questions (organized as a tree. In every node one or more questions).
These questions sould be answered with "yes " or "no".
If the bot starts to question, it can start with any question from yuor database of questions marked as a start-question. The answer is the way to the next node in the tree.
Edit: Here is a somple one written in ruby you can start with: rubyBOT
naive chatbot program. No parsing, no cleverness, just a training file and output.
It first trains itself on a text and then later uses the data from that training to generate responses to the interlocutor’s input. The training process creates a dictionary where each key is a word and the value is a list of all the words that follow that word sequentially anywhere in the training text. If a word features more than once in this list then that reflects and it is more likely to be chosen by the bot, no need for probabilistic stuff just do it with a list.
The bot chooses a random word from your input and generates a response by choosing another random word that has been seen to be a successor to its held word. It then repeats the process by finding a successor to that word in turn and carrying on iteratively until it thinks it’s said enough. It reaches that conclusion by stopping at a word that was prior to a punctuation mark in the training text. It then returns to input mode again to let you respond, and so on.
It isn’t very realistic but I hereby challenge anyone to do better in 71 lines of code !! This is a great challenge for any budding Pythonists, and I just wish I could open the challenge to a wider audience than the small number of visitors I get to this blog. To code a bot that is always guaranteed to be grammatical must surely be closer to several hundred lines, I simplified hugely by just trying to think of the simplest rule to give the computer a mere stab at having something to say.
Its responses are rather impressionistic to say the least ! Also you have to put what you say in single quotes.
I used War and Peace for my “corpus” which took a couple of hours for the training run, use a shorter file if you are impatient…
here is the trainer
#lukebot-trainer.py
import pickle
b=open('war&peace.txt')
text=[]
for line in b:
for word in line.split():
text.append (word)
b.close()
textset=list(set(text))
follow={}
for l in range(len(textset)):
working=[]
check=textset[l]
for w in range(len(text)-1):
if check==text[w] and text[w][-1] not in '(),.?!':
working.append(str(text[w+1]))
follow[check]=working
a=open('lexicon-luke','wb')
pickle.dump(follow,a,2)
a.close()
here is the bot
#lukebot.py
import pickle,random
a=open('lexicon-luke','rb')
successorlist=pickle.load(a)
a.close()
def nextword(a):
if a in successorlist:
return random.choice(successorlist[a])
else:
return 'the'
speech=''
while speech!='quit':
speech=raw_input('>')
s=random.choice(speech.split())
response=''
while True:
neword=nextword(s)
response+=' '+neword
s=neword
if neword[-1] in ',?!.':
break
print response
You tend to get an uncanny feeling when it says something that seems partially to make sense.
I would suggest looking at Bayesian probabilities. Then just monitor the chat room for a period of time to create your probability tree.
I'm not sure this is what you're looking for, but there's an old program called ELIZA which could hold a conversation by taking what you said and spitting it back at you after performing some simple textual transformations.
If I remember correctly, many people were convinced that they were "talking" to a real person and had long elaborate conversations with it.
If you're just dabbling, I believe Pidgin allows you to script chat style behavior. Part of the framework probably tacks the state of who sent the message when, and you'd want to keep a log of your bot's internal state for each of the last N messages. Future state decisions could be hardcoded based on inspection of previous states and the content of the most recent few messages. Or you could do something like the Markov chains discussed and use it both for parsing and generating.
If you do not require a learning bot, using AIML (http://www.aiml.net/) will most likely produce the result you want, at least with respect to the bot parsing input and answering based on it.
You would reuse or create "brains" made of XML (in the AIML-format) and parse/run them in a program (parser). There are parsers made in several different languages to choose from, and as far as I can tell the code seems to be open source in most cases.
You can use "ChatterBot", and host it locally using - 'flask-chatterbot-master"
Links:
[ChatterBot Installation]
https://chatterbot.readthedocs.io/en/stable/setup.html
[Host Locally using - flask-chatterbot-master]: https://github.com/chamkank/flask-chatterbot
Cheers,
Ratnakar