Duplicate from autoevaluate/leaderboards

Co-authored-by: Tristan Thrush <Tristan@users.noreply.huggingface.co>

README.md

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+---
+title: Leaderboards
+emoji: 📈
+colorFrom: red
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.10.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: autoevaluate/leaderboards
+---
+
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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+import pandas as pd
+import streamlit as st
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.repocard import metadata_load
+from utils import ascending_metrics, metric_ranges
+import numpy as np
+from st_aggrid import AgGrid, GridOptionsBuilder, JsCode
+from os.path import exists
+import threading
+
+st.set_page_config(layout="wide")
+
+
+def get_model_infos():
+    api = HfApi()
+    model_infos = api.list_models(filter="model-index", cardData=True)
+    return model_infos
+
+
+def parse_metric_value(value):
+    if isinstance(value, str):
+        "".join(value.split("%"))
+        try:
+            value = float(value)
+        except:  # noqa: E722
+            value = None
+    elif isinstance(value, list):
+        if len(value) > 0:
+            value = value[0]
+        else:
+            value = None
+    value = round(value, 4) if isinstance(value, float) else None
+    return value
+
+
+def parse_metrics_rows(meta, only_verified=False):
+    if not isinstance(meta["model-index"], list) or len(meta["model-index"]) == 0 or "results" not in meta["model-index"][0]:
+        return None
+    for result in meta["model-index"][0]["results"]:
+        if not isinstance(result, dict) or "dataset" not in result or "metrics" not in result or "type" not in result["dataset"]:
+            continue
+        dataset = result["dataset"]["type"]
+        if dataset == "":
+            continue
+        row = {"dataset": dataset, "split": "-unspecified-", "config": "-unspecified-"}
+        if "split" in result["dataset"]:
+            row["split"] = result["dataset"]["split"]
+        if "config" in result["dataset"]:
+            row["config"] = result["dataset"]["config"]
+        no_results = True
+        incorrect_results = False
+        for metric in result["metrics"]:
+            name = metric["type"].lower().strip()
+
+            if name in ("model_id", "dataset", "split", "config", "pipeline_tag", "only_verified"):
+                # Metrics are not allowed to be named "dataset", "split", "config", "pipeline_tag"
+                continue
+            value = parse_metric_value(metric.get("value", None))
+            if value is None:
+                continue
+            if name in row:
+                new_metric_better = value < row[name] if name in ascending_metrics else value > row[name]
+            if name not in row or new_metric_better:
+                # overwrite the metric if the new value is better.
+
+                if only_verified:
+                    if "verified" in metric and metric["verified"]:
+                        no_results = False
+                        row[name] = value
+                        if name in metric_ranges:
+                            if value < metric_ranges[name][0] or value > metric_ranges[name][1]:
+                                incorrect_results = True
+                else:
+                    no_results = False
+                    row[name] = value
+                    if name in metric_ranges:
+                        if value < metric_ranges[name][0] or value > metric_ranges[name][1]:
+                            incorrect_results = True
+        if no_results or incorrect_results:
+            continue
+        yield row
+
+@st.cache(ttl=0)
+def get_data_wrapper():
+
+    def get_data(dataframe=None, verified_dataframe=None):
+        data = []
+        verified_data = []
+        print("getting model infos")
+        model_infos = get_model_infos()
+        print("got model infos")
+        for model_info in model_infos:
+            meta = model_info.cardData
+            if meta is None:
+                continue
+            for row in parse_metrics_rows(meta):
+                if row is None:
+                    continue
+                row["model_id"] = model_info.id
+                row["pipeline_tag"] = model_info.pipeline_tag
+                row["only_verified"] = False
+                data.append(row)
+            for row in parse_metrics_rows(meta, only_verified=True):
+                if row is None:
+                    continue
+                row["model_id"] = model_info.id
+                row["pipeline_tag"] = model_info.pipeline_tag
+                row["only_verified"] = True
+                data.append(row)
+        dataframe = pd.DataFrame.from_records(data)
+        dataframe.to_pickle("cache.pkl")
+
+    if exists("cache.pkl"):
+        # If we have saved the results previously, call an asynchronous process
+        # to fetch the results and update the saved file. Don't make users wait
+        # while we fetch the new results. Instead, display the old results for
+        # now. The new results should be loaded when this method
+        # is called again.
+        dataframe = pd.read_pickle("cache.pkl")
+        t = threading.Thread(name="get_data procs", target=get_data)
+        t.start()
+    else:
+        # We have to make the users wait during the first startup of this app.
+        get_data()
+        dataframe = pd.read_pickle("cache.pkl")
+
+    return dataframe
+
+dataframe = get_data_wrapper()
+
+st.markdown("# 🤗 Leaderboards")
+
+query_params = st.experimental_get_query_params()
+if "first_query_params" not in st.session_state:
+    st.session_state.first_query_params = query_params
+first_query_params = st.session_state.first_query_params
+
+default_task = first_query_params.get("task", [None])[0]
+default_only_verified = bool(int(first_query_params.get("only_verified", [0])[0]))
+print(default_only_verified)
+default_dataset = first_query_params.get("dataset", [None])[0]
+default_split = first_query_params.get("split", [None])[0]
+default_config = first_query_params.get("config", [None])[0]
+default_metric = first_query_params.get("metric", [None])[0]
+
+only_verified_results = st.sidebar.checkbox(
+    "Filter for Verified Results",
+    value=default_only_verified,
+    help="Select this checkbox if you want to see only results produced by the Hugging Face model evaluator, and no self-reported results."
+)
+
+selectable_tasks = list(set(dataframe.pipeline_tag))
+if None in selectable_tasks:
+    selectable_tasks.remove(None)
+selectable_tasks.sort(key=lambda name: name.lower())
+selectable_tasks = ["-any-"] + selectable_tasks
+
+task = st.sidebar.selectbox(
+    "Task",
+    selectable_tasks,
+    index=(selectable_tasks).index(default_task) if default_task in selectable_tasks else 0,
+    help="Filter the selectable datasets by task. Leave as \"-any-\" to see all selectable datasets."
+)
+
+if task != "-any-":
+    dataframe = dataframe[dataframe.pipeline_tag == task]
+
+selectable_datasets = ["-any-"] + sorted(list(set(dataframe.dataset.tolist())), key=lambda name: name.lower())
+if "" in selectable_datasets:
+    selectable_datasets.remove("")
+
+dataset = st.sidebar.selectbox(
+    "Dataset",
+    selectable_datasets,
+    index=selectable_datasets.index(default_dataset) if default_dataset in selectable_datasets else 0,
+    help="Select a dataset to see the leaderboard!"
+)
+
+dataframe = dataframe[dataframe.only_verified == only_verified_results]
+
+current_query_params = {"dataset": [dataset], "only_verified": [int(only_verified_results)], "task": [task]}
+
+st.experimental_set_query_params(**current_query_params)
+
+if dataset != "-any-":
+    dataset_df = dataframe[dataframe.dataset == dataset]
+else:
+    dataset_df = dataframe
+
+dataset_df = dataset_df.dropna(axis="columns", how="all")
+
+if len(dataset_df) > 0:
+    selectable_configs = list(set(dataset_df["config"]))
+    selectable_configs.sort(key=lambda name: name.lower())
+
+    if "-unspecified-" in selectable_configs:
+        selectable_configs.remove("-unspecified-")
+        selectable_configs = ["-unspecified-"] + selectable_configs
+
+    if dataset != "-any-":
+        config = st.sidebar.selectbox(
+            "Config",
+            selectable_configs,
+            index=selectable_configs.index(default_config) if default_config in selectable_configs else 0,
+            help="Filter the results on the current leaderboard by the dataset config. Self-reported results might not report the config, which is why \"-unspecified-\" is an option."
+        )
+        dataset_df = dataset_df[dataset_df.config == config]
+
+        selectable_splits = list(set(dataset_df["split"]))
+        selectable_splits.sort(key=lambda name: name.lower())
+
+        if "-unspecified-" in selectable_splits:
+            selectable_splits.remove("-unspecified-")
+            selectable_splits = ["-unspecified-"] + selectable_splits
+
+        split = st.sidebar.selectbox(
+            "Split",
+            selectable_splits,
+            index=selectable_splits.index(default_split) if default_split in selectable_splits else 0,
+            help="Filter the results on the current leaderboard by the dataset split. Self-reported results might not report the split, which is why \"-unspecified-\" is an option."
+        )
+
+        current_query_params.update({"config": [config], "split": [split]})
+
+        st.experimental_set_query_params(**current_query_params)
+
+        dataset_df = dataset_df[dataset_df.split == split]
+
+    not_selectable_metrics = ["model_id", "dataset", "split", "config", "pipeline_tag", "only_verified"]
+    selectable_metrics = list(filter(lambda column: column not in not_selectable_metrics, dataset_df.columns))
+
+    dataset_df = dataset_df.filter(["model_id"] + (["dataset"] if dataset == "-any-" else []) + selectable_metrics)
+    dataset_df = dataset_df.dropna(thresh=2)  # Want at least two non-na values (one for model_id and one for a metric).
+
+    sorting_metric = st.sidebar.radio(
+        "Sorting Metric",
+        selectable_metrics,
+        index=selectable_metrics.index(default_metric) if default_metric in selectable_metrics else 0,
+        help="Select the metric to sort the leaderboard by. Click on the metric name in the leaderboard to reverse the sorting order."
+    )
+
+    current_query_params.update({"metric": [sorting_metric]})
+
+    st.experimental_set_query_params(**current_query_params)
+
+    st.markdown(
+        "Please click on the model's name to be redirected to its model card."
+    )
+
+    st.markdown(
+        "Want to beat the leaderboard? Don't see your model here? Simply request an automatic evaluation [here](https://huggingface.co/spaces/autoevaluate/model-evaluator)."
+    )
+
+    st.markdown(
+        "If you do not see your self-reported results here, ensure that your results are in the expected range for all metrics. E.g., accuracy is 0-1, not 0-100."
+    )
+
+    if dataset == "-any-":
+        st.info(
+            "Note: you haven't chosen a dataset, so the leaderboard is showing the best scoring model for a random sample of the datasets available."
+        )
+
+    # Make the default metric appear right after model names and dataset names
+    cols = dataset_df.columns.tolist()
+    cols.remove(sorting_metric)
+    sorting_metric_index = 1 if dataset != "-any-" else 2
+    cols = cols[:sorting_metric_index] + [sorting_metric] + cols[sorting_metric_index:]
+    dataset_df = dataset_df[cols]
+
+    # Sort the leaderboard, giving the sorting metric highest priority and then ordering by other metrics in the case of equal values.
+    dataset_df = dataset_df.sort_values(by=cols[sorting_metric_index:], ascending=[metric in ascending_metrics for metric in cols[sorting_metric_index:]])
+    dataset_df = dataset_df.replace(np.nan, '-')
+
+    # If dataset is "-any-", only show the best model for a random sample of 100 datasets.
+    # Otherwise The leaderboard is way too long and doesn't give the users a feel for all of
+    # the datasets available for a task.
+    if dataset == "-any-":
+        filtered_dataset_df_dict = {column: [] for column in dataset_df.columns}
+        seen_datasets = set()
+        for _, row in dataset_df.iterrows():
+            if row["dataset"] not in seen_datasets:
+                for column in dataset_df.columns:
+                    filtered_dataset_df_dict[column].append(row[column])
+                seen_datasets.add(row["dataset"])
+        dataset_df = pd.DataFrame(filtered_dataset_df_dict)
+        dataset_df = dataset_df.sample(min(100, len(dataset_df)))
+
+    # Make the leaderboard
+    gb = GridOptionsBuilder.from_dataframe(dataset_df)
+    gb.configure_default_column(sortable=False)
+    gb.configure_column(
+        "model_id",
+        cellRenderer=JsCode('''function(params) {return '<a target="_blank" href="https://huggingface.co/'+params.value+'">'+params.value+'</a>'}'''),
+    )
+    if dataset == "-any-":
+        gb.configure_column(
+            "dataset",
+            cellRenderer=JsCode('''function(params) {return '<a target="_blank" href="https://huggingface.co/spaces/autoevaluate/leaderboards?dataset='+params.value+'">'+params.value+'</a>'}'''),
+        )
+    for name in selectable_metrics:
+        gb.configure_column(name, type=["numericColumn","numberColumnFilter","customNumericFormat"], precision=4, aggFunc='sum')
+
+    gb.configure_column(
+        sorting_metric,
+        sortable=True,
+        cellStyle=JsCode('''function(params) { return {'backgroundColor': '#FFD21E'}}''')
+    )
+
+    go = gb.build()
+    fit_columns = len(dataset_df.columns) < 10
+    AgGrid(dataset_df, gridOptions=go, height=28*len(dataset_df) + (35 if fit_columns else 41), allow_unsafe_jscode=True, fit_columns_on_grid_load=fit_columns, enable_enterprise_modules=False)
+
+else:
+    st.markdown(
+        "No " + ("verified" if only_verified_results else "unverified") + " results to display. Try toggling the verified results filter."
+    )

requirements.txt

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+pandas==1.5.1
+huggingface_hub==0.11.1
+numpy==1.23.4
+streamlit-aggrid==0.3.3

utils.py

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+ascending_metrics = {
+    "wer",
+    "cer",
+    "loss",
+    "mae",
+    "mahalanobis",
+    "mse",
+    "perplexity",
+    "ter",
+}
+
+metric_ranges = {
+    "accuracy": (0,1),
+    "precision": (0,1),
+    "recall": (0,1),
+    "macro f1": (0,1),
+    "micro f1": (0,1),
+    "pearson": (-1, 1),
+    "matthews_correlation": (-1, 1),
+    "spearmanr": (-1, 1),
+    "google_bleu": (0, 1),
+    "precision@10": (0, 1),
+    "mae": (0, 1),
+    "mauve": (0, 1),
+    "frontier_integral": (0, 1),
+    "mean_iou": (0, 1),
+    "mean_accuracy": (0, 1),
+    "overall_accuracy": (0, 1),
+    "meteor": (0, 1),
+    "mse": (0, 1),
+    "perplexity": (0, float("inf")),
+    "rogue1": (0, 1),
+    "rogue2": (0, 1),
+    "sari": (0, 100),
+}