Markov-switching dynamic factor model (MS-DFM) for Recession Probabilities / Python

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• Python File Tagging System does not retrieve nested dictionaries in dictionary

  I am building a file tagging system using Python. The idea is simple. Given a directory of files (and files within subdirectories), I want to filter them out using a filter input and tag those files with a word or a phrase.

  If I got the following contents in my current directory:

  data/
     budget.xls
     world_building_budget.txt
  a.txt
  b.exe
  hello_world.dat
  world_builder.spec
  

  and I execute the following command in the shell:

  py -3 tag_tool.py -filter=world -tag="World-Building Tool"
  

  My output will be:

  These files were tagged with "World-Building Tool":
  data/ world_building_budget.txt
  hello_world.dat
  world_builder.spec
  

  My current output isn't exactly like this but basically, I am converting all files and files within subdirectories into a single dictionary like this:

  def fs_tree_to_dict(path_):
      file_token = ''
      for root, dirs, files in os.walk(path_):
          tree = {d: fs_tree_to_dict(os.path.join(root, d)) for d in dirs}
          tree.update({f: file_token for f in files})
          return tree
  

  Right now, my dictionary looks like this: key:''.

  In the following function, I am turning the empty values '' into empty lists (to hold my tags):

  def empty_str_to_list(d):
      for k,v in d.items():
          if v == '':
              d[k] = []
          elif isinstance(v, dict):
              empty_str_to_list(v)
  

  When I run my entire code, this is my output:

  hello_world.dat ['World-Building Tool']
  world_builder.spec ['World-Building Tool']
  

  But it does not see data/world_building_budget.txt. This is the full dictionary:

  {'data': {'world_building_budget.txt': []},  
  'a.txt': [],
  'hello_world.dat': [], 
  'b.exe': [], 
  'world_builder.spec': []}
  
  

  This is my full code:

  import os, argparse
  
  
  def fs_tree_to_dict(path_):
      file_token = ''
      for root, dirs, files in os.walk(path_):
          tree = {d: fs_tree_to_dict(os.path.join(root, d)) for d in dirs}
          tree.update({f: file_token for f in files})
          return tree
  
  
  def empty_str_to_list(d):
      for k, v in d.items():
          if v == '':
              d[k] = []
          elif isinstance(v, dict):
              empty_str_to_list(v)
  
  
  parser = argparse.ArgumentParser(description="Just an example", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  
  parser.add_argument("--filter", action="store", help="keyword to filter files")
  parser.add_argument("--tag", action="store", help="a tag phrase to attach to a file")
  parser.add_argument("--get_tagged", action="store", help="retrieve files matching an existing tag")
  
  args = parser.parse_args()
  
  filter = args.filter
  tag = args.tag
  get_tagged = args.get_tagged
  current_dir = os.getcwd()
  
  files_dict = fs_tree_to_dict(current_dir)
  empty_str_to_list(files_dict)
  
  for k, v in files_dict.items():
      if filter in k:
          if v == []:
              v.append(tag)
              print(k, v)
  
          elif isinstance(v, dict):
              empty_str_to_list(v)
  
      if get_tagged in v:
          print(k, v)
  
• Actaully i am working on a project and in it, it is showing no module name pip_internal plz help me for the same. I am using pycharm(conda interpreter

   File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 196, in _run_module_as_main
      return _run_code(code, main_globals, None,
    File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in _run_code
      exec(code, run_globals)
    File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\Scripts\pip.exe\__main__.py", line 4, in <module>
    File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\lib\site-packages\pip\_internal\__init__.py", line 4, in <module>
      from pip_internal.utils import _log
  

  I am using pycharm with conda interpreter.

  
• Looping the function if the input is not string

  I'm new to python (first of all) I have a homework to do a function about checking if an item exists in a dictionary or not.

  inventory = {"apple" : 50, "orange" : 50, "pineapple" : 70, "strawberry" : 30}
  
  def check_item():
      x = input("Enter the fruit's name: ")
      if not x.isalpha():
          print("Error! You need to type the name of the fruit")
      elif x in inventory:
          print("Fruit found:", x)
          print("Inventory available:", inventory[x],"KG")
      else:
          print("Fruit not found")
  check_item()
  

  I want the function to loop again only if the input written is not string. I've tried to type return Under print("Error! You need to type the name of the fruit") but didn't work. Help
• Hidden Markov Model for sequential data

  Is it possible to predict next digit in a sequence using an hmm? For example I have the dataset as follows: [0, 3, 4, 1] [1, 3, 4, 2] etc.

  After training my model with the data described above I want to be able to predict the next digit for every number that I get as input. i.e if 0 is the input I want to know how possible it is to get 1 or 2 or 3... after that. Also I don't know the transition matrix and the probabilities of each next state from the start. I guess I have to compute them too.
• How can I extract transition matrix for non-homogeneous HMM model built with depmixS4?

  I created the following non-homogeneous Hidden Markov Model using depmix:

  #primary time series
  datats<-ts(data$primary)
  y<- as.numeric(datats)
  
  #Preparing covariates
  cov1ts<-ts(data$cov1)
  x1<- as.numeric(cov1ts)
  
  cov2ts<-ts(data$cov2)
  x2<- as.numeric(cov2ts)
  
  #Build model
  hmm_model <- depmix(y~1, data = data.frame(y), nstates = 2, transition = ~ (scale(x1)+scale(x2)))
  
  hmm_model <-fit(hmm_model)
  summary(hmm_model)
  

  I now want to make a prediction about the next state. In the past I did this using homogeneous HMM as explained in this post: How to predict out-of-sample observations with depmixS4 package in R?

  Specifically, in my case I did:

  #[...] Created homogeneous model "hom_model" like before but without transition parameter
  
  #transition probabilities at states
  mat1<-hom_model@transition[[1]]@parameters$coefficients 
  mat1<-hom_model@transition[[2]]@parameters$coefficients
  #transition matrix
  transmat<-rbind(mat1, mat2) 
  
  # prediction as described in post, not very relevant for this question
  

  But now for non-homogeneous hmm, I cannot obtain the transition matrix in the same way because now when I obtain mat1 and mat2, I get the coefficients of the covariates and intercept for each state. Specifically, my output for mat1 in the non-hom case looks like this:

              St1        St2        
  (Intercept)   0 -0.6704946 
  scale(x1)     0 -1.7279190  
  scale(x2)     0 -2.0905961
  

  I am unsure on how to obtain the transition matrix for the non-homogeneous case, and also a bit confused as why the State 1 coefficients are all 0.

  Thank you