src/display/utils.py

from dataclasses import dataclass, make_dataclass
from enum import Enum

import pandas as pd

from src.about import Tasks, nc_tasks, nr_tasks, lp_tasks

def fields(raw_class):
    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]


# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass
class ColumnContent:
    name: str
    type: str
    displayed_by_default: bool
    hidden: bool = False
    never_hidden: bool = False

## Leaderboard columns
auto_eval_column_dict = []
# Init
auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
#Scores
auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
for task in Tasks:
    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
# Model information
auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])

# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)


auto_eval_column_dict_nc = []
auto_eval_column_dict_nc.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict_nc.append(["average_rank", ColumnContent, ColumnContent("Average Rank⬆️", "number", True)])
for task in nc_tasks:
    auto_eval_column_dict_nc.append(['_'.join(task.value.col_name.split('-')), ColumnContent, ColumnContent(task.value.benchmark, "number", True)])
auto_eval_column_dict_nc.append(["author", ColumnContent, ColumnContent("Author", "markdown", True, never_hidden=False)])
auto_eval_column_dict_nc.append(["email", ColumnContent, ColumnContent("Email", "markdown", True, never_hidden=False)])
auto_eval_column_dict_nc.append(["Paper_URL", ColumnContent, ColumnContent("Paper URL", "markdown", True, never_hidden=False)])
auto_eval_column_dict_nc.append(["Github_URL", ColumnContent, ColumnContent("Github URL", "markdown", True, never_hidden=False)])
auto_eval_column_dict_nc.append(["Time", ColumnContent, ColumnContent("Time", "markdown", True, never_hidden=False)])
#auto_eval_column_dict_nc.append(["num_of_Params", ColumnContent, ColumnContent("# of Params", "markdown", True, never_hidden=False)])

AutoEvalColumn_NodeClassification = make_dataclass("AutoEvalColumn_NodeClassification", auto_eval_column_dict_nc, frozen=True)


auto_eval_column_dict_nr = []
auto_eval_column_dict_nr.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict_nr.append(["average_rank", ColumnContent, ColumnContent("Average Rank⬆️", "number", True)])
for task in nr_tasks:
    auto_eval_column_dict_nr.append(['_'.join(task.value.col_name.split('-')), ColumnContent, ColumnContent(task.value.benchmark, "number", True)])
auto_eval_column_dict_nr.append(["author", ColumnContent, ColumnContent("Author", "markdown", True, never_hidden=False)])
auto_eval_column_dict_nr.append(["email", ColumnContent, ColumnContent("Email", "markdown", True, never_hidden=False)])
auto_eval_column_dict_nr.append(["Paper_URL", ColumnContent, ColumnContent("Paper URL", "markdown", True, never_hidden=False)])
auto_eval_column_dict_nr.append(["Github_URL", ColumnContent, ColumnContent("Github URL", "markdown", True, never_hidden=False)])
auto_eval_column_dict_nr.append(["Time", ColumnContent, ColumnContent("Time", "markdown", True, never_hidden=False)])
#auto_eval_column_dict_nr.append(["num_of_Params", ColumnContent, ColumnContent("# of Params", "markdown", True, never_hidden=False)])

AutoEvalColumn_NodeRegression = make_dataclass("AutoEvalColumn_NodeRegression", auto_eval_column_dict_nr, frozen=True)


auto_eval_column_dict_lp = []
auto_eval_column_dict_lp.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
auto_eval_column_dict_lp.append(["average_rank", ColumnContent, ColumnContent("Average Rank⬆️", "number", True)])
for task in lp_tasks:
    auto_eval_column_dict_lp.append(['_'.join(task.value.col_name.split('-')), ColumnContent, ColumnContent(task.value.benchmark, "number", True)])
auto_eval_column_dict_lp.append(["author", ColumnContent, ColumnContent("Author", "markdown", True, never_hidden=False)])
auto_eval_column_dict_lp.append(["email", ColumnContent, ColumnContent("Email", "markdown", True, never_hidden=False)])
auto_eval_column_dict_lp.append(["Paper_URL", ColumnContent, ColumnContent("Paper URL", "markdown", True, never_hidden=False)])
auto_eval_column_dict_lp.append(["Github_URL", ColumnContent, ColumnContent("Github URL", "markdown", True, never_hidden=False)])
auto_eval_column_dict_lp.append(["Time", ColumnContent, ColumnContent("Time", "markdown", True, never_hidden=False)])
#auto_eval_column_dict_lp.append(["num_of_Params", ColumnContent, ColumnContent("# of Params", "markdown", True, never_hidden=False)])

AutoEvalColumn_LinkPrediction = make_dataclass("AutoEvalColumn_LinkPrediction", auto_eval_column_dict_lp, frozen=True)


## For the queue columns in the submission tab
@dataclass(frozen=True)
class EvalQueueColumn:  # Queue column
    model = ColumnContent("model", "markdown", True)
    revision = ColumnContent("revision", "str", True)
    private = ColumnContent("private", "bool", True)
    precision = ColumnContent("precision", "str", True)
    weight_type = ColumnContent("weight_type", "str", "Original")
    status = ColumnContent("status", "str", True)

## All the model information that we might need
@dataclass
class ModelDetails:
    name: str
    display_name: str = ""
    symbol: str = "" # emoji


class ModelType(Enum):
    PT = ModelDetails(name="pretrained", symbol="🟢")
    FT = ModelDetails(name="fine-tuned", symbol="🔶")
    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
    RL = ModelDetails(name="RL-tuned", symbol="🟦")
    Unknown = ModelDetails(name="", symbol="?")

    def to_str(self, separator=" "):
        return f"{self.value.symbol}{separator}{self.value.name}"

    @staticmethod
    def from_str(type):
        if "fine-tuned" in type or "🔶" in type:
            return ModelType.FT
        if "pretrained" in type or "🟢" in type:
            return ModelType.PT
        if "RL-tuned" in type or "🟦" in type:
            return ModelType.RL
        if "instruction-tuned" in type or "⭕" in type:
            return ModelType.IFT
        return ModelType.Unknown

class OFFICIAL(Enum):
    official = ModelDetails("Official")
    unofficial = ModelDetails("Unofficial")

class HONOR(Enum):
    yes = ModelDetails("Yes")
    no = ModelDetails("No")

class TASK_LIST(Enum):
    amazon_user_churn = ModelDetails("rel-amazon-user-churn")
    amazon_item_churn = ModelDetails("rel-amazon-item-churn")
    amazon_user_ltv = ModelDetails("rel-amazon-user-ltv")
    amazon_item_ltv = ModelDetails("rel-amazon-item-ltv")
    amazon_user_item_purchase = ModelDetails("rel-amazon-user-item-purchase")
    amazon_user_item_rate = ModelDetails("rel-amazon-user-item-rate")
    amazon_user_item_review = ModelDetails("rel-amazon-user-item-review")

    # rel-stack
    stack_user_engagement = ModelDetails("rel-stack-user-engagement")
    stack_user_badge = ModelDetails("rel-stack-user-badge")
    stack_post_votes = ModelDetails("rel-stack-post-votes")
    stack_user_post_comment = ModelDetails("rel-stack-user-post-comment")
    stack_user_post_related = ModelDetails("rel-stack-user-post-related")

    # rel-trial
    trial_study_outcome = ModelDetails("rel-trial-study-outcome")
    trial_study_adverse = ModelDetails("rel-trial-study-adverse")
    trial_site_success = ModelDetails("rel-trial-site-success")
    trial_condition_sponsor_run = ModelDetails("rel-trial-condition-sponsor-run")
    trial_site_sponsor_run = ModelDetails("rel-trial-site-sponsor-run")

    # rel-f1
    f1_driver_position = ModelDetails("rel-f1-driver-position")
    f1_driver_dnf = ModelDetails("rel-f1-driver-dnf")
    f1_driver_top3 = ModelDetails("rel-f1-driver-top3")

    # rel-hm
    hm_user_churn = ModelDetails("rel-hm-user-churn")
    hm_item_sales = ModelDetails("rel-hm-item-sales")
    hm_user_item_purchase = ModelDetails("rel-hm-user-item-purchase")

    # rel-event
    event_user_repeat = ModelDetails("rel-event-user-repeat")
    event_user_ignore = ModelDetails("rel-event-user-ignore")
    event_user_attendance = ModelDetails("rel-event-user-attendance")

    # rel-avito
    avito_user_visits = ModelDetails("rel-avito-user-visits")
    avito_user_clicks = ModelDetails("rel-avito-user-clicks")
    avito_ads_clicks = ModelDetails("rel-avito-ads-clicks")
    avito_user_ad_visit = ModelDetails("rel-avito-user-ad-visit")

class WeightType(Enum):
    Adapter = ModelDetails("Adapter")
    Original = ModelDetails("Original")
    Delta = ModelDetails("Delta")

class Precision(Enum):
    float16 = ModelDetails("float16")
    bfloat16 = ModelDetails("bfloat16")
    float32 = ModelDetails("float32")
    #qt_8bit = ModelDetails("8bit")
    #qt_4bit = ModelDetails("4bit")
    #qt_GPTQ = ModelDetails("GPTQ")
    Unknown = ModelDetails("?")

    def from_str(precision):
        if precision in ["torch.float16", "float16"]:
            return Precision.float16
        if precision in ["torch.bfloat16", "bfloat16"]:
            return Precision.bfloat16
        if precision in ["float32"]:
            return Precision.float32
        #if precision in ["8bit"]:
        #    return Precision.qt_8bit
        #if precision in ["4bit"]:
        #    return Precision.qt_4bit
        #if precision in ["GPTQ", "None"]:
        #    return Precision.qt_GPTQ
        return Precision.Unknown

# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
COLS_NC = [c.name for c in fields(AutoEvalColumn_NodeClassification) if not c.hidden]
COLS_NR = [c.name for c in fields(AutoEvalColumn_NodeRegression) if not c.hidden]
COLS_LP = [c.name for c in fields(AutoEvalColumn_LinkPrediction) if not c.hidden]

TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]

EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]

BENCHMARK_COLS = [t.value.col_name for t in Tasks]

NUMERIC_INTERVALS = {
    "?": pd.Interval(-1, 0, closed="right"),
    "~1.5": pd.Interval(0, 2, closed="right"),
    "~3": pd.Interval(2, 4, closed="right"),
    "~7": pd.Interval(4, 9, closed="right"),
    "~13": pd.Interval(9, 20, closed="right"),
    "~35": pd.Interval(20, 45, closed="right"),
    "~60": pd.Interval(45, 70, closed="right"),
    "70+": pd.Interval(70, 10000, closed="right"),
}