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ds/trec09mq/stat.py

@@ -1,31 +1,34 @@
 import pandas as pd
+import pickle
+import seaborn as sns
+import matplotlib.pyplot as plt
 
-def read_refinements(all, dataset, querytype):
 
- matrix = [[0 for x in range(col)] for y in range(row)]
- for q in range(len(result)):
- df_all=pd.read_csv(all, encoding="ISO-8859-1", sep=',')
+def read_refinements(all, dataset, querytype):
+ df_all = pd.read_csv(all, encoding="ISO-8859-1", sep=',')
  R = pd.read_csv(all, encoding="ISO-8859-1", sep=',').columns[4:len(df_all.columns):3]
- df_results= pd.read_csv(dataset, encoding="ISO-8859-1", sep=',')
- df_qt=pd.read_csv(querytype, encoding="ISO-8859-1", sep='\t',usecols =['Topic', 'Class'])
- df_qt['Topic']=df_qt['Topic'].astype(int)
+ df_results = pd.read_csv(dataset, encoding="ISO-8859-1", sep=',')
+ df_qt = pd.read_csv(querytype, encoding="ISO-8859-1", sep='\t', usecols=['Topic', 'Class'])
+ df_qt['Topic'] = df_qt['Topic'].astype(int)
  df_qt.rename(columns=({'Topic': 'qid'}), inplace=True)
  df = pd.merge(df_results, df_qt, on='qid', how='inner')
  QT = df_qt['Class'].unique()
 
- A=dict()
+ A = dict()
  for qt in QT:
- A[qt]=dict()
- for r in R: A[qt][r]=0
+ A[qt] = dict()
+ for r in R: A[qt][r] = 0
 
- for index,row in df.iterrows():
- r_n=row['star_model_count']*3
- for i in range(1,r_n,3):
- r=str(row[i+3])
- qt=row["Class"]
- A[qt][r] +=1
+ for index, row in df.iterrows():
+ r_n = row['star_model_count'] * 3
+ for i in range(1, r_n, 3):
+ r = str(row[i + 3])
+ qt = row["Class"]
+ A[qt][r] += 1
 
  print(A)
 
+
 if __name__ == "__main__":
- read_refinements('../../qe/output/trec09mq/topics.trec09mq.bm25.map.all.csv','../../qe/output/trec09mq/topics.trec09mq.bm25.map.dataset.csv','./queryclasses')
+ read_refinements('../../qe/output/trec09mq/topics.trec09mq.bm25.map.all.csv',
+ '../../qe/output/trec09mq/topics.trec09mq.bm25.map.dataset.csv', './queryclasses')