Duplicate from andrewkroening/nba-war-predictor

Co-authored-by: Andrew Kroening <andrewkroening@users.noreply.huggingface.co>

.github/workflows/hugging_face_cicd.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}        
+        run: git push https://andrewkroening:$HF_TOKEN@huggingface.co/spaces/andrewkroening/nba-war-predictor main

.github/workflows/main.yml

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+
+name: Continuous Integration
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: install packages
+        run: make install
+      - name: lint
+        run: make lint
+      - name: format
+        run: make format

LICENSE

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Makefile

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+install:
+	pip install --upgrade pip &&\
+		pip install -r requirements.txt
+
+lint:
+	pylint --disable=R,C --extension-pkg-whitelist='pydantic' app.py
+
+format:
+	black *.py
+
+all: install lint format

README.md

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+---
+title: Nba War Predictor
+emoji: 🏀
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.6
+app_file: app.py
+pinned: true
+license: cc
+duplicated_from: andrewkroening/nba-war-predictor
+---
+
+# Hello and welcome to this NBA WAR Predictor Tool
+
+[![Continuous Integration](https://github.com/andrewkroening/nba-war-predictor-tool/actions/workflows/main.yml/badge.svg)](https://github.com/andrewkroening/nba-war-predictor-tool/actions/workflows/main.yml)    [![Sync to Hugging Face hub](https://github.com/andrewkroening/nba-war-predictor-tool/actions/workflows/hugging_face_cicd.yml/badge.svg)](https://github.com/andrewkroening/nba-war-predictor-tool/actions/workflows/hugging_face_cicd.yml)
+
+[You can find the NBA WAR Predictor here.](https://huggingface.co/spaces/andrewkroening/nba-war-predictor)
+
+[Here's the demo video where I walk through the tool](https://youtu.be/gKk0_YpTQ90)
+
+## General
+
+In the NBA, Wins Above Replacement (WAR) is one of many metrics used to measure player performance. WAR denotes how much better (or worse) a player is when compared to an average level replacement player of the same position. Higher WARs are better, and typically will cluster closer to values in the single digits, with all-star players reaching into the high-20s in exceptional cases.
+
+This tool will allow a user to select a season and then a player from those who played in the NBA that year to see that player's performance to that point plus a five year projection. A season dropdown allows the user to select a new season to see how the projection changes over time.
+
+## Functionality
+
+This is a simple tool to exercise a few functions that are available from HuggingFace. They are:
+
+* [Ingest a datasource from HuggingFace.](https://huggingface.co/datasets/andrewkroening/538-NBA-Historical-Raptor) This dataset was built from a dataset available from [fivethirtyeight.com.](https://github.com/fivethirtyeight/data/tree/master/nba-raptor)
+
+* Use the Prophet package to construct a five-year prediction from the performance of a player up to a designated point (I won't be trying this again with this type of data).
+
+* Set up a HuggingFace Space and use Gradio to deploy a simple web app.
+
+* Add some user functionality to the app to improve experience and engagement.

app.py

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+"""A simple gradio app to predict NBA player performance this season"""
+
+import gradio as gr
+import pandas as pd
+from prophet import Prophet
+from datasets import load_dataset
+
+pd.options.plotting.backend = "plotly"
+
+
+# initialize empty players
+players = [""]
+
+# initialize empty seasons
+seasons = [1977, 2021]
+
+# load data
+nba_dataset = load_dataset("andrewkroening/538-NBA-Historical-Raptor")
+
+# initialize a dataframe from the nba dataset
+nba_df = pd.DataFrame(nba_dataset["train"])
+
+# make a player_df with seasons and every player for that season
+player_df = nba_df[["season", "player_name"]].copy()
+
+
+def get_players(this_season, df=player_df):
+    """Get the players for a given season"""
+    # get the players for the given season
+    season_players = df[df["season"] == this_season]["player_name"].unique().tolist()
+
+    return gr.Dropdown.update(choices=season_players), gr.update(visible=False)
+
+
+def get_forecast(this_year, this_player):
+    """Get the forecast for a given player and year and the performance for entire career"""
+    # load data
+    nba_data_fore = load_dataset("andrewkroening/538-NBA-Historical-Raptor")
+
+    # initialize a dataframe from the nba dataset
+    df = pd.DataFrame(nba_data_fore["train"])
+
+    # truncate to the player selected
+    dataset = df[df["player_name"] == this_player]
+    player_data = dataset[["season", "war_total"]].copy()
+    # player_perform = player_df.copy()
+
+    # make a list of the seasons the player played in
+    player_seasons = player_data["season"].unique().tolist()
+
+    # make two dfs, one for actual performance and one for the model
+    # player_perform = player_perform[player_perform["season"] <= year + 5]
+    player_data = player_data[player_data["season"] <= this_year]
+
+    # convert the season column to a datetime object
+    player_data["season"] = pd.to_datetime(player_data["season"], format="%Y")
+
+    # set the df for prophet
+    player_data.columns = ["ds", "y"]
+    player_data = player_data.sort_values("ds")
+
+    # build the prophet model
+    m = Prophet(seasonality_mode="multiplicative").fit(player_data)
+    future = m.make_future_dataframe(periods=5, freq="Y")
+    forecast = m.predict(future)
+
+    # plot the forecast
+    fig1 = m.plot(forecast, xlabel="Year", ylabel="Wins Above Replacement")
+
+    # plot the actual performance
+    # fig2 = player_perform.plot(
+    #     x="season", y="war_total", title="Actual Performance", template="plotly_white")
+
+    # return the figure
+
+    return fig1, gr.Dropdown.update(choices=player_seasons), gr.update(visible=True)
+
+
+with gr.Blocks() as demo:
+
+    gr.Markdown(
+        """
+    ### This is a slightly comical NBA Player Performance Predictor.
+
+    ***It is designed to show a projection for performance (Wins Above Replacement) and compare it to the actual performance over a career.***
+    
+    ***If the projection hangs, it is because the model is taking a long time to run. Refresh the page and give it another shot...get it?***
+    """
+    )
+    with gr.Row():
+        year = gr.Slider(1977, 2021, label="Season", interactive=True, step=1)
+        player = gr.Dropdown(players, label="Player", interactive=True)
+
+    with gr.Column(visible=False) as output_col:
+        gr.Markdown(
+            "**Below is the player forecast for the selected season plus 5 years. Next to the graph is a dropdown you can use to change the season and update the chart and see how a player's projection has changed over time.**"
+        )
+        with gr.Row():
+            plt = gr.Plot()
+            season = gr.Dropdown(seasons, label="Season", interactive=True, step=1)
+
+    year.change(get_players, inputs=year, outputs=[player, output_col])
+    player.change(
+        get_forecast, inputs=[year, player], outputs=[plt, season, output_col]
+    )
+    season.change(
+        get_forecast, inputs=[season, player], outputs=[plt, season, output_col]
+    )
+
+demo.launch()

requirements.txt

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+black
+pylint
+ipython
+pandas
+gradio
+prophet
+plotly
+datasets
+huggingface