Merge pull request #177 from sunya-ch/exporter

add missing writer file

manifests/kepler/patch/patch-ci.yaml

@@ -17,3 +17,4 @@ spec:
       containers:
       - name: kepler-exporter
         imagePullPolicy: IfNotPresent
+        image: quay.io/sustainable_computing_io/kepler:latest-libbpf

src/train/exporter/writer.py

@@ -0,0 +1,166 @@
+import os
+import sys
+
+import pandas as pd
+
+util_path = os.path.join(os.path.dirname(__file__), '..', '..', 'util')
+sys.path.append(util_path)
+
+from loader import load_json, get_machine_path, default_init_model_url, get_url, trainers_with_weight
+from config import ERROR_KEY
+from train_types import ModelOutputType, FeatureGroup
+
+def write_markdown(markdown_filepath, markdown_content):
+
+    try:
+        with open(markdown_filepath, "w", encoding="utf-8") as markdown_file:
+            # Write the Markdown content to the file
+            markdown_file.write(markdown_content)
+            print(f"Markdown file '{markdown_filepath}' has been created successfully.")
+    except IOError as e:
+        print(f"Cannot write '{markdown_filepath}': {e}")
+
+
+# Function to convert a dictionary to a Markdown table
+def dict_to_markdown_table(data):
+    # Get the column headers
+    headers = list(data.keys())
+
+    # Initialize the Markdown table with headers
+    markdown_table = "| " + " | ".join(headers) + " |\n"
+    markdown_table += "| " + " | ".join(["---"] * len(headers)) + " |\n"
+
+    # Iterate through the dictionary and add rows to the table
+    for i in range(len(data[headers[0]])):
+        row = "| " + " | ".join([str(data[key][i]) for key in headers]) + " |\n"
+        markdown_table += row
+
+    return markdown_table
+
+def format_cpe_content(data):
+    spec = data["spec"]
+    iterations = spec["iterationSpec"]["iterations"]
+    items = dict()
+    for iteration in iterations:
+        items[iteration["name"]] = iteration["values"]
+    df = pd.DataFrame(items)
+    content = dict_to_markdown_table(df)
+    content += "\nrepetition: {}".format(spec["repetition"])
+    return content
+
+def format_trainer(trainers):
+    trainer_content = ""
+    for trainer in trainers.split(","):
+        trainer_content += "  - {}\n".format(trainer)
+    return trainer_content
+
+def _version_path(machine_path):
+    return os.path.join(machine_path, "..")
+
+def generate_pipeline_page(data_path, machine_path, train_args, skip_if_exist=True):
+    doc_path = os.path.join(_version_path(machine_path), ".doc")
+    pipeline_name = train_args["pipeline_name"]
+    markdown_filename = "{}.md".format(pipeline_name)
+    markdown_filepath = os.path.join(doc_path, markdown_filename)
+    if skip_if_exist and os.path.exists(markdown_filepath):
+        print(f"Markdown file '{markdown_filepath}' already exists.")
+        return
+
+    workload_content = ""
+    inputs = train_args["input"].split(",")
+    for input in inputs:
+        benchmark_name = "".join(input.split("_")[0:-2])
+        data = load_json(data_path, benchmark_name)
+
+        workload_content += """
+### {}
+
+<-- put workload description here -->
+
+<details>
+
+{}
+
+</details>
+
+        """.format(benchmark_name, format_cpe_content(data))
+
+
+    markdown_content = """
+# Pipeline {} 
+
+## Description
+
+<-- put pipeline description here -->
+
+## Components
+
+- **Extractor:** {}
+- **Isolator:** {}
+- **AbsPower Trainers:**
+
+{}
+
+- **DynPower Trainers:** 
+
+{}
+
+## Workload information
+
+{}
+    """.format(pipeline_name, train_args["extractor"], train_args["isolator"], format_trainer(train_args["abs_trainers"]), "  (same as AbsPower Trainers)" if train_args["abs_trainers"] == train_args["dyn_trainers"] else train_args["dyn_trainers"], workload_content)
+
+    write_markdown(markdown_filepath, markdown_content)
+
+def model_url(version, machine_id, pipeline_name, energy_source, output_type, feature_group, model_name, weight):
+    machine_path = get_machine_path(default_init_model_url, version, machine_id, assure=False)
+    model_url = get_url(ModelOutputType[output_type], FeatureGroup[feature_group], model_name=model_name, model_topurl=machine_path, energy_source=energy_source, pipeline_name=pipeline_name, weight=weight)
+    return model_url
+
+def format_error_content(train_args, mae_validated_df_map, weight):
+    content = ""
+    for energy_source, mae_validated_df_outputs in mae_validated_df_map.items():
+        for output_type, mae_validated_df in mae_validated_df_outputs.items():
+            content += "### {} {} model\n\n".format(energy_source, output_type)
+            df = mae_validated_df
+            if weight:
+                df = mae_validated_df[mae_validated_df["model_name"].str.contains('|'.join(trainers_with_weight))]
+            items = []
+            min_err_rows = df.loc[df.groupby(["feature_group"])[ERROR_KEY].idxmin()]
+            for _, row in min_err_rows.iterrows():
+                item = dict()
+                feature_group = row["feature_group"]
+                model_name = row["model_name"]
+                item["url"] = model_url(train_args["version"], train_args["machine_id"], train_args["pipeline_name"], energy_source, output_type, feature_group, model_name, weight)
+                item[ERROR_KEY] = "{:.2f}".format(row[ERROR_KEY])
+                item["feature group"] = feature_group
+                item["model name"] = model_name
+                items += [item]
+            print_df = pd.DataFrame(items, columns=["feature group", "model name", ERROR_KEY, "url"])
+            content += dict_to_markdown_table(print_df.sort_values(by=["feature group"]))
+    return content
+
+def generate_validation_results(machine_path, train_args, mae_validated_df_map):
+    markdown_filepath = os.path.join(machine_path, "README.md")
+
+    markdown_content = "# Validation results\n\n"
+    markdown_content += "## With local estimator\n\n"
+    markdown_content += format_error_content(train_args, mae_validated_df_map, weight=True)
+    markdown_content += "## With sidecar estimator\n\n"
+    markdown_content += format_error_content(train_args, mae_validated_df_map, weight=False)
+    write_markdown(markdown_filepath, markdown_content)
+
+def append_version_readme(machine_path, train_args, pipeline_metadata, include_raw):
+    readme_path = os.path.join(_version_path(machine_path), "README.md")
+
+    content_to_append = "{0}|[{1}](./.doc/{1}.md)|{2}|{3}|{4}|[{5}](https://github.com/{5})|[link](./{6}/README.md)\n".format(train_args["machine_id"],  \
+           train_args["pipeline_name"], \
+           "&check;" if include_raw else "X", \
+           pipeline_metadata["collect_time"], \
+           pipeline_metadata["last_update_time"], \
+           pipeline_metadata["publisher"],\
+           train_args["machine_id"]\
+           )
+
+    with open(readme_path, 'a') as file:
+        file.write(content_to_append)