Push Sysbench RW and experiment codes

LeeJYg · Nov 9, 2024 · 7b489c3 · 7b489c3
1 parent 5ea6889
commit 7b489c3
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diff --git a/VAETune/plot.ipynb b/VAETune/plot.ipynb
diff --git a/VAETune/space.py b/VAETune/space.py
@@ -32,46 +32,65 @@ def __repr__(self) -> str:
     r"\s*(\w+), Type: (\w+), Range: \[(\d+), (\d+)\], Default: (\d+)"
 )
 if __name__ == "__main__":
-    s = "sbrw_smac"
-    with open(f"repo/history_{s}.json") as f:
-        j = json.load(f)["data"]
-        c = sorted(j, key=lambda x: x["external_metrics"].get("tps", 0))[-1]
-        pprint.pprint((c["configuration"], c["external_metrics"]))
-    with open(f"repo/history_{s}_ground_truth.json") as f:
-        j = json.load(f)["data"]
-        c = sorted(j, key=lambda x: x["external_metrics"].get("tps", 0))[-1]
-        pprint.pprint((c["configuration"], c["external_metrics"]))
-    with open(f"logs/DBTune-{s}.log") as f:
+    for s in [
+        # "oltpbench_tatp",
+        # "oltpbench_tpcc",
+        # "oltpbench_wikipedia",
+        # "oltpbench_ycsb",
+        # "sbread",
+        "sbrw",
+        # "sbwrite",
+        # "twitter",
+    ]:
+        with open(f"repo/history_{s}_smac.json") as f:
+            j = json.load(f)["data"]
+            c = sorted(j, key=lambda x: x["external_metrics"].get("tps", 0))[-1]
+            pprint.pprint((c["configuration"], c["external_metrics"]))
+        with open(f"repo/history_{s}_smac_ground_truth.json") as f:
+            j = json.load(f)["data"]
+            c = sorted(j, key=lambda x: x["external_metrics"].get("tps", 0))[-1]
+            pprint.pprint((c["configuration"], c["external_metrics"]))
         c = c["configuration"]
-        config_text = f.read()
-        config_blocks = CONFIG_BLOCK_PATTERN.findall(config_text)
-        d = defaultdict(list)
-        for block in config_blocks:
-            hyperparameters = []
-            for match in HYPERPARAMETER_PATTERN.findall(block):
-                name, param_type, range_start, range_end, default = match
-                if param_type != "UniformInteger":
-                    pprint.pprint(param_type)
-                if int(range_start) <= c[name] <= int(range_end):
-                    d[name].append(1)
-                else:
-                    d[name].append(0)
-                hyperparameters.append(
-                    Hyperparameter(
-                        name=name,
-                        param_type=param_type,
-                        param_range=[int(range_start), int(range_end)],
-                        default=int(default),
+        l = []
+        with open(f"scripts/experiment/gen_knobs/mysql_all_197_32G.json") as f:
+            j2 = json.load(f)
+            for k in j2:
+                if j2[k]["type"] == "enum":
+                    if c[k] == j2[k]["default"]:
+                        l.append(1)
+                    else:
+                        l.append(0)
+        print(sum(l)/len(l))
+        with open(f"logs/DBTune-{s}_smac.log") as f:
+            config_text = f.read()
+            config_blocks = CONFIG_BLOCK_PATTERN.findall(config_text)
+            d = defaultdict(list)
+            for block in config_blocks:
+                hyperparameters = []
+                for match in HYPERPARAMETER_PATTERN.findall(block):
+                    name, param_type, range_start, range_end, default = match
+                    if param_type != "UniformInteger":
+                        pprint.pprint(param_type)
+                    if int(range_start) <= c[name] <= int(range_end):
+                        d[name].append(1)
+                    else:
+                        d[name].append(0)
+                    hyperparameters.append(
+                        Hyperparameter(
+                            name=name,
+                            param_type=param_type,
+                            param_range=[int(range_start), int(range_end)],
+                            default=int(default),
+                        )
                     )
-                )
-            # pprint.pprint(hyperparameters)
-    pd.DataFrame(
-        map(lambda x: (x[0], len(x[1]), sum(x[1]) / len(x[1])), d.items()),
-        columns=["key", "times", "proportion"],
-    ).to_csv(f"{s}.csv", index=False)
-    print(d)
-    # pprint.pprint(
-    #     list(
-    #         map(lambda x: (x[0], x[1]["external_metrics"].get("tps", 0)), enumerate(j))
-    #     )
-    # )
+                # pprint.pprint(hyperparameters)
+        pd.DataFrame(
+            map(lambda x: (x[0], len(x[1]), sum(x[1]) / len(x[1])), d.items()),
+            columns=["key", "times", "proportion"],
+        ).to_csv(f"{s}.csv", index=False)
+        # print(d)
+        pprint.pprint(
+            list(
+                map(lambda x: (x[0], x[1]["external_metrics"].get("tps", 0)), enumerate(j))
+            )
+        )