getting the topic distributions for all documents

text_analysis/contrib_file_topic_distributions.csv

@@ -0,0 +1,453 @@
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+meck.git_CONTRIBUTING.md,0.11793644158225002,0.2099319903730411,0.6711136876390819,0.0010178804056269353
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text_analysis/topicModel.py

@@ -3,6 +3,7 @@ import numpy as np
 import pandas as pd
 import glob
 import copy
+import csv
 from statistics import mean, median
 from strip_markdown import strip_markdown
 import joblib
@@ -125,8 +126,8 @@ def get_most_prevalent(vect_documents, documents):
     return most_prevalent
 
 def prevalent_topics(vect_documents, file_list):
-    #lda = joblib.load('0509_readme_lda.jl')
+    lda = joblib.load('0509_readme_lda.jl')
-    lda = joblib.load('0514_contrib_lda.jl')
+    #lda = joblib.load('0514_contrib_lda.jl')
     distributions = lda.transform(vect_documents)
     #figuring out what the max distribution is and then figuring out the mode
     top_topic = []
@@ -143,6 +144,18 @@ def prevalent_topics(vect_documents, file_list):
     most_frequent(top_topic)
     print(count_of_multiple)
     df = pd.DataFrame(topic_arrays)
+    #finding the distribution values for all documents
+    with open('readme_file_topic_distributions.csv', 'w', newline='') as csvfile:
+        fieldnames = ['filename', 't0', 't1', 't2', 't3', 't4', 't5', 't6', 't7']
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        writer.writeheader()
+        for i, row in df.iterrows():
+            project_dir =  {}
+            project_dir['filename'] = file_list[i].split("/")[-1]
+            array_row = df.iloc[i].to_numpy()
+            for j in range(8):
+                project_dir["t" + str(j)] = array_row[j]
+            writer.writerow(project_dir)
     #print(df.sort_values(by=['0']).head(5))
     '''
     for i in range(8):
@@ -172,9 +185,9 @@ def most_frequent(topic_prevalence):
 
 
 if __name__ == "__main__":
-    #readme_directory = "/data/users/mgaughan/kkex/time_specific_files/dwo_partitioned_readme/p2"
+    readme_directory = "/data/users/mgaughan/kkex/time_specific_files/readme3"
-    contributing_directory = "/data/users/mgaughan/kkex/time_specific_files/dwo_partitioned_contributing/p2"
+    #contributing_directory = "/data/users/mgaughan/kkex/time_specific_files/contributing3"
-    listed_corpus, wordcounts, wordlengths, file_list = get_data_from_dir(contributing_directory)
+    listed_corpus, wordcounts, wordlengths, file_list = get_data_from_dir(readme_directory)
     print("Mean wordcount: ", mean(wordcounts))
     print("Median wordcount: ", median(wordcounts))
     print("Mean wordlength: ", mean(wordlengths))
@@ -189,8 +202,10 @@ if __name__ == "__main__":
                             )
     data_vectorized = vectorizer.fit_transform(lemmatized_corpus)
     '''
-    vectorizer = joblib.load('contrib_vectorizer.jl')
+    #vectorizer = joblib.load('contrib_vectorizer.jl')
+    vectorizer = joblib.load('readme_vectorizer.jl')
     data_vectorized = vectorizer.transform(lemmatized_corpus)     
+    #print(data_vectorized)           
     #lda_model_identification(data_vectorized)
     #topic_distributions = best_lda_model(data_vectorized, vectorizer.get_feature_names_out())
     #get_most_prevalent(data_vectorized, file_list)