add plot method and other minor edits

src/trustyai/utils/extras/metrics_service.py

@@ -5,6 +5,7 @@
 import datetime as dt
 import pandas as pd
 import requests
+import matplotlib.pyplot as plt
 
 from trustyai.utils.api.api import TrustyAIApi
 
@@ -128,7 +129,7 @@ def print_name_mapping(self):
                 f"{self.trusty_url}/info/names",
                 json=payload,
                 headers=self.headers,
-                verify=True,
+                verify=self.verify,
                 timeout=timeout,
             )
             if response.status_code == 200:
@@ -182,27 +183,60 @@ def upload_data_to_model(self, model_name: str, json_file: str, timeout=5):
                 return response.text
             raise RuntimeError(f"Error {response.status_code}: {response.reason}")
 
-    def get_metric_data(
-        self, namespace: str, metric: str, time_interval: List[str], timeout=5
-    ):
+    def get_metric_data(self, metric: str, time_interval: List[str], timeout=5):
         """
-        Retrives metric data for a specific range in time
+        Retrives metric data for a specific range in time for each subcategory in data field
         """
-        params = {"query": f"{metric}{{namespace='{namespace}'}}{time_interval}"}
-        response = requests.get(
-            f"{self.thanos_url}/api/v1/query?",
-            params=params,
-            headers=self.headers,
-            verify=self.verify,
-            timeout=timeout,
-        )
-        if response.status_code == 200:
-            data_dict = json.loads(response.text)["data"]["result"][0]["values"]
-            metric_df = pd.DataFrame(data_dict, columns=["timestamp", metric])
-            metric_df["timestamp"] = metric_df["timestamp"].apply(
-                lambda epoch: dt.datetime.fromtimestamp(epoch).strftime(
-                    "%Y-%m-%d %H:%M:%S"
-                )
+        metric_df = pd.DataFrame()
+        for subcategory in list(
+            self.get_model_metadata()[0]["data"]["inputSchema"]["nameMapping"].values()
+        ):
+            params = {
+                "query": f"{metric}{{subcategory='{subcategory}'}}{time_interval}"
+            }
+
+            response = requests.get(
+                f"{self.thanos_url}/api/v1/query?",
+                params=params,
+                headers=self.headers,
+                verify=self.verify,
+                timeout=timeout,
             )
-            return metric_df
-        raise RuntimeError(f"Error {response.status_code}: {response.reason}")
+            if response.status_code == 200:
+                if "timestamp" in metric_df.columns:
+                    pass
+                else:
+                    metric_df["timestamp"] = [
+                        item[0]
+                        for item in json.loads(response.text)["data"]["result"][0][
+                            "values"
+                        ]
+                    ]
+                metric_df[subcategory] = [
+                    item[1]
+                    for item in json.loads(response.text)["data"]["result"][0]["values"]
+                ]
+            else:
+                raise RuntimeError(f"Error {response.status_code}: {response.reason}")
+
+        metric_df["timestamp"] = metric_df["timestamp"].apply(
+            lambda epoch: dt.datetime.fromtimestamp(epoch).strftime("%Y-%m-%d %H:%M:%S")
+        )
+        return metric_df
+
+
+    @staticmethod
+    def plot_metric(metric_df: pd.DataFrame, metric: str):
+        """
+        Plots a line for each subcategory in the pandas DataFrame returned by get_metric_request
+        with the timestamp on x-axis and specified metric on the y-axis
+        """
+        plt.figure(figsize=(12, 5))
+        for col in metric_df.columns[1:]:
+            plt.plot(metric_df["timestamp"], metric_df[col])
+        plt.xlabel("timestamp")
+        plt.ylabel(metric)
+        plt.xticks(rotation=45)
+        plt.legend(metric_df.columns[1:])
+        plt.tight_layout()
+        plt.show()