Python script that increases a number by a percent

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• Sparse Matrix Creation : KeyError: 579 for text datasets

  I am trying to use the make_sparse_matrix function to create a sparse matrix for my text dataset, and I face KeyError: 579. Does anyone has any leads on the root of the error.

  def make_sparse_matrix(df, indexed_words, labels):
  """
  Returns sparse matrix as dataframe.
  
  df: A dataframe with words in the columns with a document id as an index (X_train or X_test)
  indexed_words: index of words ordered by word id
  labels: category as a series (y_train or y_test)
  """
  
  nr_rows = df.shape[0]
  nr_cols = df.shape[1]
  word_set = set(indexed_words)
  dict_list = []
  
  for i in range(nr_rows):
      for j in range(nr_cols):
          
          word = df.iat[i, j]
          if word in word_set:
              doc_id = df.index[i]
              word_id = indexed_words.get_loc(word)
              category = labels.at[doc_id]
              
              item = {'LABEL': category, 'DOC_ID': doc_id,
                     'OCCURENCE': 1, 'WORD_ID': word_id}
              
              dict_list.append(item)
  
  return pd.DataFrame(dict_list)
  
  
  
  make_sparse_matrix( X_train, word_index,  y_test ) 
  

  X_train is a DF that contains one single word in each cell, word_index contains all the index of words and y_test stores all labels.

  The Key Error I am facing is:

  --------------------------------------------------------------------------- KeyError Traceback (most recent call last) ~\New folder\envs\geo_env\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 3079 try: -> 3080 return self._engine.get_loc(casted_key) 3081 except KeyError as err:

  pandas_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

  pandas_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

  pandas_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

  pandas_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

  KeyError: 579

  The above exception was the direct cause of the following exception:

  KeyError Traceback (most recent call last) in

  in make_sparse_matrix(df, indexed_words, labels) 20 doc_id = df.index[i] 21 word_id = indexed_words.get_loc(word) ---> 22 category = labels.at[doc_id] 23 24 item = {'LABEL': category, 'DOC_ID': doc_id,

  ~\New folder\envs\geo_env\lib\site-packages\pandas\core\indexing.py in getitem(self, key) 2154 return self.obj.loc[key] 2155 -> 2156 return super().getitem(key) 2157 2158 def setitem(self, key, value):

  ~\New folder\envs\geo_env\lib\site-packages\pandas\core\indexing.py in getitem(self, key) 2101 2102 key = self._convert_key(key) -> 2103 return self.obj._get_value(*key, takeable=self._takeable) 2104 2105 def setitem(self, key, value):

  ~\New folder\envs\geo_env\lib\site-packages\pandas\core\series.py in _get_value(self, label, takeable) 959 960 # Similar to Index.get_value, but we do not fall back to positional --> 961 loc = self.index.get_loc(label) 962 return self.index._get_values_for_loc(self, loc, label) 963

  ~\New folder\envs\geo_env\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 3080 return self._engine.get_loc(casted_key) 3081 except KeyError as err: -> 3082 raise KeyError(key) from err 3083 3084 if tolerance is not None:

  KeyError: 579
• Finding part of string in list of strings

  GCM = ([519,520,521,522,533],[534,525],[526,527,530,531], [4404])
  
  slice = int(str(df["CGM"][row_count])[:3])
  

  I am looking through a row in a csv file and taking out the number I want. i want the number that starts with the number I have in GCM. since they represent info I want in other columns. this has working fine with the slice function because all the number i wanted started with 3 digits. now that i need to look for any number that starts with 4404 and later on going to probably need to look for 57052 the slice function no longer work.

  is there a way I can, instead of slicing and comparing to list, can take 5 digit number and see if part of it is in list. preferably look for it starting 3 or more same digits. the real point of that part of code is finding out which list in GCM list the number is. it need to be able to have the number 44042 and know that the part of it a care about is in GCM[3], but on the other side do not want it to say that 32519 is in DCM[0] since I only care about number that start with 519 not ends with it.

  

  ps. I am norwegian and have been learning programming by myself. been some long nights. so something here can be lost in translation.
• How to forecast a time series out-of-sample using an ARIMA model in Python?

  I have seen similar questions at Stackoverflow. But, either the questions were different enough or if similar, they actually have not been answered. I gather it is something that modelers run into often, and have a challenge solving.

  In my case I am using two variables, one Y and one X with 50 time series sequential observations. They are both random numbers representing % changes (they could be anything you want, their true value does not matter. This is just to set up an example of my coding problem). Here are my basic codes to build this ARIMAX(1,0,0) model.

  import pandas as pd
  import statsmodels.api as sm
  import statsmodels.formula.api as smf
  
  df = pd.read_excel('/Users/gaetanlion/Google Drive/Python/Arima/df.xlsx', sheet_name = 'final')
  
  from statsmodels.tsa.arima_model import ARIMA
  endo = df['y']
  exo = df['x']
  

  Next, I build the ARIMA model, using the first 41 observations

  modelho = sm.tsa.arima.ARIMA(endo.loc[0:40], exo.loc[0:40], order =(1,0,0)).fit()
  print(modelho.summary())
  

  So far everything works just fine.

  Next, I attempt to forecast or predict the next 9 observations out-of-sample. Here I want to use the X values over these 9 observations to predict Y. And, I just can't do it. I am showing below just the one code, that I think gets me the closest to where I need to go.

  modelho.predict(exo.loc[41:49], start = 41, end = 49, dynamic = False)
  TypeError: predict() got multiple values for argument 'start'
  
• two functions from different packages not working together

  So I am doing work on ruin probability. I am performing monte-carlo simulations through R. The package that works perfectly for this is 'ruin'. You simply specify inputs for different ruin models. I am trying to simulate a SparreAndersen model where there are two random variable generators from different distributions. Typical distributions such as exponential or gamma work fine, using rgamma or rexp.

  The problem that I am having is that I require a mixed exponential function as one of the r.v. generators. I found a great package that achieves this, called 'gendist' where I use the rmixt function to generate r.v.s from a mixed exponential function.

  Running rmixt with all parameters works fine, giving a correct and accurate numerical output. Running rexp with all parameters works fine, giving a correct and accurate numerical output. Running a SparreAndersen() function works fine when using any function that is NOT the rmixt function.

  The problem: SparreAndersen does not like to use rmixt from 'gendist' package as a r.v. generator

  When I try to run this function, I get this as an error

  I have no idea why this isn't working??? See my entire code below:

  library(ruin)
  library(gendist)
  
  rmixt(1, phi=1, spec1="exp", arg1=list(rate=3), spec2="exp",
        arg2=list(rate=7) )
  
  rexp(1, rate=1)
  
  modelSA <- SparreAndersen(initial_capital = 1,
                            premium_rate = 5,
                            claim_interarrival_generator = rmixt,
                            claim_interarrival_parameters = list(1, phi=1, spec1="exp", arg1=list(rate=3), spec2="exp", arg2=list(rate=7)),
                            claim_size_generator = rexp,
                            claim_size_parameters = list(rate=1))
  

  edit: I have found a working solution, not involving the 'gendist' package. Let me know if this is the best way to go about doing this.

  My new working code:

  # Function which generates multiple exponential
  rmixedexp <- function(...) {
    choice <- c(...)
    return(rexp(n=1, sample(choice, 1, replace=TRUE)))
  }
  
  modelSA <- SparreAndersen(initial_capital = 5,
                           premium_rate = 5,
                           claim_interarrival_generator = rmixedexp,
                           claim_interarrival_parameters = list(1, 3, 7),
                           claim_size_generator = rexp,
                           claim_size_parameters = list(rate=1))
  

  I had to make my own function which takes any number of parameters, for example, 2. Each parameter is the rate of the exponential, chosen randomly (probability half).
• How to calculate the total needed to hit a target

  This might just boil down to a simple math problem but alas, I'm stuck.

  The problem - Agent 1 received 9 customer surveys so far, 7 of which are positive, therefore Agent 1 hit a satisfaction rate of 77.8%. The target is 95%, how many more surveys are needed in order to hit target, assuming all new surveys are positive?

  In a spreadsheet, I can calculate this simply by adding 1 to each row, until I hit a 95% pass rate, which in the example is 31 (Sheet Example).

  I feel so stupid for posting this but I'm hoping there is a math method of getting the answer, without having to go through each of the possible outcomes manually until hitting the target %.
• How to calculate math operations like normal calculator?

  Probably this will be silly question but could you explain me how I could perform regular math operations in JS code to be exactly matched normal calculator's result in Windows or other OS?

  Given an example:

  5.1 + 0.3168
  

  • JS gives me 5.416799999999999 result
  • Windows calculator gives me 5.4168 result

  I know that I could incorporate precision and get desired result in JS like that (5.1 + 0.3168).toFixed(4). But that won't work with other bigger numbers.

  How does Windows do that? How can perform math operations like that.

  Thanks