def check_engine_quality(self, query_num, list_of_docs):
"""
:param query_num:
:param list_of_docs:
:return: no return. prints metrics of the query. precision, recall, map.
"""
benchmark_path = "data\\benchmark_lbls_train.csv"
df = pd.read_csv(benchmark_path)
df_prec = df[df['query'] == query_num]
df_prec = df_prec[df_prec['tweet'].isin(list_of_docs)]
dict_for_data = df_prec.set_index('tweet')['y_true'].to_dict()
rmv_lst = []
ranking = []
# Add to list for rank
for doc in list_of_docs:
try:
ranking.append(dict_for_data[int(doc)])
except:
rmv_lst.append(doc)
for d in rmv_lst:
list_of_docs.remove(d)
data_df = pd.DataFrame({
'query': query_num,
'tweet': list_of_docs,
'y_true': ranking
})
df_rec = df[df['query'] == query_num]
recall_total = len(df_rec[df_rec['y_true'] == 1.0])
# print("total Relevant doc found with tag 1 :" , len (data_df[data_df['y_true'] == 1.0]))
# print("total NON relevant doc found with tag 0 :" , len (data_df[data_df['y_true'] == 0]))
# print("found total of", len(df_prec), "tagged docs")
# Calculate and print
prec5 = metrics.precision_at_n(data_df, query_num, 5)
prec10 = metrics.precision_at_n(data_df, query_num, 10)
prec50 = metrics.precision_at_n(data_df, query_num, 50)
prec_total = metrics.precision(data_df, True, query_number=query_num)
map_of_query = metrics.map(data_df)
recall_val = metrics.recall_single(data_df, recall_total, query_num)
self.map_list.append(map_of_query)
self.prec5_list.append(prec5)
self.prec10_list.append(prec10)
self.prec50_list.append(prec50)
self.prec_total_list.append(prec_total)
self.recall_list.append(recall_val)
print()
print("precision at 5 of query", query_num, "is :", prec5)
print("precision at 10 of query", query_num, "is :", prec10)
print("precision at 50 of query", query_num, "is :", prec50)
print("precision of query", query_num, "is :", prec_total)
print("recall of query", query_num, "is :", recall_val)
print("map of query", query_num, "is :", map_of_query)
'precision@5': [],
'precision@10': [],
'precision@50': [],
'recall': []
}
for query_num in range(1, 36):
dict_data['query'].append(query_num)
dict_data['recall'].append(
metrics.recall_single(q_results_labeled,
q2n_relevant.get(query_num),
query_num))
dict_data['precision'].append(
metrics.precision(q_results_labeled, True,
query_num))
dict_data['precision@5'].append(
metrics.precision_at_n(q_results_labeled,
query_num, 5))
dict_data['precision@10'].append(
metrics.precision_at_n(q_results_labeled,
query_num, 10))
dict_data['precision@50'].append(
metrics.precision_at_n(q_results_labeled,
query_num, 50))
df_data = pd.DataFrame(dict_data,
columns=[
'query', 'precision',
'precision@5', 'precision@10',
'precision@50', 'recall'
])
# print(df_data)
df_data.to_excel(engine_module + "_output.xlsx")
# test that the average across queries of precision,