perf analysis of phi-3 results

.gitattributes

@@ -33,3 +33,69 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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+data/results/Phi-3-mini-128k-instruct_mm_true.csv filter=lfs diff=lfs merge=lfs -text
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+data/results/Phi-3-mini-128k-instruct_mm_false_RP_1.000.csv filter=lfs diff=lfs merge=lfs -text
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app_modules/utils.py

@@ -1,9 +1,9 @@
 # -*- coding:utf-8 -*-
 from __future__ import annotations
 
-import json
 import logging
 import os
+import math
 import platform
 import re
 from pathlib import Path
@@ -13,6 +13,8 @@ import requests
 import torch
 from tqdm import tqdm
 from langchain.memory import ConversationSummaryBufferMemory
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mtick
 
 
 class LogRecord(logging.LogRecord):
@@ -276,3 +278,146 @@ def detect_repetition_scores(text, debug=False):
         text, debug=debug
     )
     return pd.Series([newline_score, repetition_score, total_repetitions])
+
+
+def load_with_newline_and_repetition_scores(result_file, force_recalculate=False):
+    print(f"loading result file: {result_file}")
+    df = pd.read_csv(result_file, comment="#", on_bad_lines="warn")
+
+    if (
+        force_recalculate
+        or "newline_score" not in df.columns
+        or "repetition_score" not in df.columns
+        or "total_repetitions" not in df.columns
+    ):
+        df[["newline_score", "repetition_score", "total_repetitions"]] = df[
+            "answer"
+        ].apply(detect_scores)
+        df.to_csv(result_file, index=False)
+
+    return df
+
+
+def replace_last(source_string, old_string, new_string):
+    head, _sep, tail = source_string.rpartition(old_string)
+    return head + new_string + tail
+
+
+df_ms_macro = pd.read_json("./data/datasets/ms_macro.json")
+
+
+def load_for_repetition_penalty_ms_macro(
+    csv_result_file, repetition_penalty, force_recalculate=False
+):
+    result_file = replace_last(
+        csv_result_file, ".csv", f"_RP_{repetition_penalty:.3f}.csv"
+    )
+    df = load_with_newline_and_repetition_scores(
+        result_file, force_recalculate=force_recalculate
+    )
+
+    if df["ground_truth"][0] != df_ms_macro["wellFormedAnswers"][0]:
+        df["ground_truth"] = df_ms_macro["wellFormedAnswers"]
+        print("ground_truth updated for:", result_file)
+        df.to_csv(result_file, index=False)
+    return df
+
+
+def adjust_perf_scores_with_repetition_penalty(result, precision, recall):
+    newline_score = [
+        df["newline_score"].mean() for df in result["df_list_repetition_penalty"]
+    ]
+    print(f"newline_score: {newline_score}")
+
+    repetition_score = [
+        df["repetition_score"].mean() for df in result["df_list_repetition_penalty"]
+    ]
+    print(f"repetition_score: {repetition_score}")
+
+    precision = [
+        f / math.log10(10 + n + r)
+        for f, n, r in zip(precision, newline_score, repetition_score)
+    ]
+    recall = [
+        f / math.log10(10 + n + r)
+        for f, n, r in zip(recall, newline_score, repetition_score)
+    ]
+
+    return precision, recall
+
+
+# MS MACRO
+def plot_performance_scores_ms_macro(
+    result,
+    models=None,
+    title="Performance",
+):
+
+    if models is None:
+        models = result.keys()
+    for model in models:
+        print(f"model: {model}")
+        df = result[model]["df_overall"]
+        # print(result[model]["df_list_repetition_penalty"][0].describe())
+
+        # Calculate the statistics
+        bleu1 = list(df["bleu1"])
+        rougeL = list(df["rougeL"])
+        f1 = [2 * (p * r) / (p + r) for p, r in zip(bleu1, rougeL)]
+        best_f1 = max(f1)
+        best_f1_index = f1.index(best_f1)
+
+        bleu1, rougeL = adjust_perf_scores_with_repetition_penalty(
+            result[model], bleu1, rougeL
+        )
+        afrp = [2 * (p * r) / (p + r) for p, r in zip(bleu1, rougeL)]
+
+        # f1 = [df["f1"].mean() for df in result[model]["df_list_repetition_penalty"]]
+        best_afrp = max(afrp)
+        best_afrp_index = afrp.index(best_afrp)
+
+        repetition_penalties = list(df["repetition_penalty"])
+
+        # line plot for precision, recall, f1
+        plt.figure(figsize=(10, 6))
+
+        plt.axvspan(
+            repetition_penalties[best_f1_index] - 0.01,
+            repetition_penalties[best_f1_index] + 0.01,
+            alpha=0.5,
+            edgecolor="none",
+            facecolor="blue",
+        )
+
+        plt.axvspan(
+            repetition_penalties[best_afrp_index] - 0.01,
+            repetition_penalties[best_afrp_index] + 0.01,
+            alpha=0.5,
+            edgecolor="none",
+            facecolor="orange",
+        )
+
+        plt.plot(
+            repetition_penalties,
+            f1,
+            label="Overall Perf Score",
+            marker="D",
+            color="blue",
+        )
+        plt.plot(
+            repetition_penalties,
+            afrp,
+            label="RF Adjusted Perf Score",
+            marker="o",
+            color="orange",
+        )
+
+        plt.xlabel("Repetition Penalties")
+        plt.ylabel("Score")
+        plt.xlim(0.99, 1.31)
+        # y in percentage
+        plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
+        plt.title(f"{model} {title}")
+        plt.legend(bbox_to_anchor=(1.0, 0.5), loc="center left")
+
+        plt.show()

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