Separating accuracy evaluation code

eval_accuracy.py

@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+
+import os
+import asyncio as aio
+import requests
+import pandas as pd
+from iso639 import languages
+import aiohttp
+from asyncio_pool import AioPool
+
+API_URL = "http://localhost:8000"
+
+
+def getSupportedLanguages():
+    allLangs = requests.get(API_URL + "/api/language/predict").json()
+    allLangs = allLangs["supported_languages"]
+    return allLangs
+
+
+def getTestData():
+    cacheFile = "./test.csv"
+    testDataUrl = "https://huggingface.co/datasets/papluca/language-identification/raw/main/test.csv"
+
+    supportedLangs = getSupportedLanguages()
+    if os.path.exists(cacheFile):
+        data = pd.read_csv(cacheFile)
+    else:
+        data = pd.read_csv(testDataUrl)
+        data.to_csv(cacheFile)
+    data.rename(columns={"labels": "language", "text": "Text"}, inplace=True)
+    data["language"] = data["language"].apply(lambda code: languages.part1[code].name)
+    data = data[data["language"].isin(supportedLangs)]
+    return data
+
+
+async def task(row):
+    i = row[0]
+    row = row[1]
+    url = API_URL + "/api/language/predict"
+    body = {"text": row["Text"]}
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(url, json=body) as r:
+            json_body = await r.json()
+            return [json_body, row]
+
+
+async def evaluateAccuracy():
+    data = getTestData()
+    stats = {}
+    failed = []
+    pool = AioPool(20)
+
+    results = await pool.map(task, data.iterrows())
+    print("complted %d requests " % len(data))
+    for [resp_body, row] in results:
+        lang = row["language"]
+        if lang not in stats:
+            stats[lang] = {"total": 0, "failed": 0}
+        if not resp_body["lang"] == lang:
+            stats[lang]["failed"] += 1
+            failed.append(row)
+
+        stats[lang]["total"] += 1
+
+    stats = pd.DataFrame(stats).T
+    stats["success_percent"] = (1 - (stats["failed"] / stats["total"])) * 100.0
+    stats.sort_values("success_percent", ascending=False, inplace=True)
+    print(stats)
+    return stats
+
+
+if __name__ == "__main__":
+    loop = aio.new_event_loop()
+    loop.run_until_complete(evaluateAccuracy())

test.py

@@ -1,12 +1,6 @@
 #!/usr/bin/env python
 
-import os
 import requests
-import pandas as pd
-import numpy as np
-from iso639 import languages
-from asyncio_pool import AioPool
-import asyncio as aio
 
 API_URL = "http://localhost:8000"
 
@@ -27,64 +21,3 @@ def test_post_language_detection():
     assert response.status_code == 200
     resp_body = response.json()
     assert resp_body["lang"] == "English"
-
-
-def getSupportedLanguages():
-    allLangs = requests.get(API_URL + "/api/language/predict").json()
-    allLangs = allLangs["supported_languages"]
-    return allLangs
-
-
-def getTestData():
-    cacheFile = "./test.csv"
-    testDataUrl = "https://huggingface.co/datasets/papluca/language-identification/raw/main/test.csv"
-
-    supportedLangs = getSupportedLanguages()
-    if os.path.exists(cacheFile):
-        data = pd.read_csv(cacheFile)
-    else:
-        data = pd.read_csv(testDataUrl)
-        data.to_csv(cacheFile)
-    data.rename(columns={"labels": "language", "text": "Text"}, inplace=True)
-    data["language"] = data["language"].apply(lambda code: languages.part1[code].name)
-    data = data[data["language"].isin(supportedLangs)]
-    return data
-
-
-async def task(row):
-    row = row[1]
-    response = requests.post(
-        API_URL + "/api/language/predict", json={"text": row["Text"]}
-    )
-    resp_body = response.json()
-    return [resp_body, row]
-
-
-async def evaluateAccuracy():
-    data = getTestData()
-    stats = {}
-    failed = []
-    pool = AioPool(10)
-
-    results = await pool.map(task, data.iterrows())
-    print('complted %d requests ' % len(data))
-    for [resp_body, row] in results:
-        lang = row["language"]
-        if lang not in stats:
-            stats[lang] = {"total": 0, "failed": 0}
-        if not resp_body["lang"] == lang:
-            stats[lang]["failed"] += 1
-            failed.append(row)
-
-        stats[lang]["total"] += 1
-
-    stats = pd.DataFrame(stats).T
-    stats["success_percent"] = (1 - (stats["failed"] / stats["total"])) * 100.0
-    stats.sort_values("success_percent", ascending=False, inplace=True)
-    print(stats)
-    return stats
-
-
-if __name__ == "__main__":
-    loop = aio.new_event_loop()
-    loop.run_until_complete(evaluateAccuracy())