skelo 0.1.5
A scikit-learn interface to the Elo and Glicko2 rating systems
Requires Python: >=3

# skelo

<p align="left">
<a href="https://github.com/mbhynes/skelo/actions"><img alt="Tests" src="https://github.com/mbhynes/skelo/workflows/Tests/badge.svg"></a>
<a href="https://skelo.readthedocs.io"><img alt="Docs" src="https://readthedocs.org/projects/skelo/badge/?version=latest"></a>
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</p>

An implementation of the [Elo](https://en.wikipedia.org/wiki/Elo_rating_system) and [Glicko2](https://en.wikipedia.org/wiki/Glicko_rating_system) rating systems with a [scikit-learn](https://scikit-learn.org/stable/)-compatible interface.

The [skelo](https://pypi.org/project/skelo/) package is a simple implementation suitable for small-scale rating systems that fit into memory on a single machine.
It's intended to provide a convenient API for creating Elo/Glicko ratings in a data science & analytics workflow for small games on the scale thousands of players and millions of matches, primarily as a means of feature transformation in other `sklearn` pipelines or benchmarking classifier accuracy.

## Motivation

What problem does this package solve?

Despite there being many rating system implementations available (e.g. [sublee/elo](https://github.com/sublee/elo/) [ddm7018/Elo](https://github.com/ddm7018/Elo), [rshk/elo](https://github.com/rshk/elo), [EloPy](https://github.com/HankSheehan/EloPy), [PythonSkills](https://github.com/McLeopold/PythonSkills), [pyglicko2](https://github.com/ryankirkman/pyglicko2), [glicko2](https://github.com/deepy/glicko2), [glicko](https://github.com/sublee/glicko)) it's hard to find one that satisfies several criteria:
  - A simple and clean API that's convenient for a data-driven model development loop, for which use case the scikit-learn estimator [interface](https://scikit-learn.org/stable/modules/classes.html) is the *de facto* standard
  - Explicit management of intervals of validity for ratings, such that as matches occur a timeseries of ratings is evolved for each player (i.e. type-2 data management as opposed to type-1 fire-and-forget ratings)

This package addresses this gap by providing rating system implementations with:
  - a simple interface for in-memory data management (i.e. storing the ratings as they evolve)
  - time-aware ratings retrieval (i.e. *resolving* a player to their respective rating at an arbitrary point in time)
  - scikit-learn classifier methods to interact with the predictions in a (more) typical data science workflow

## Installation

Install via the PyPI package [skelo](https://pypi.org/project/skelo/) using pip:
```python
pip3 install skelo
```

Alternatively, a bleeding edge release is available from GitHub directly:
```python
pip3 install git+https://github.com/mbhynes/skelo.git
```


Further reference information on the API is hosted on [skelo.readthedocs.io](https://skelo.readthedocs.io/en/latest/).

## Quickstart

- As a quickstart, we can load and fit an `EloEstimator` (classifier) on some sample tennis data:
```python
import numpy as np
import pandas as pd
from skelo.model.elo import EloEstimator

df = pd.read_csv("https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_1979.csv")
labels = len(df) * [1] # the data are ordered as winner/loser

model = EloEstimator(
  key1_field="winner_name",
  key2_field="loser_name",
  timestamp_field="tourney_date",
  initial_time=19781231,
).fit(df, labels)
```

- The ratings data are available as a `pandas DataFrame` if we wish to do any further analysis on it:
```python
>>> model.rating_model.to_frame()

                     key       rating  valid_from    valid_to
0               Tim Vann  1500.000000    19781231  19790115.0
1               Tim Vann  1490.350941    19790115         NaN
2     Alejandro Gattiker  1500.000000    19781231  19790917.0
3     Alejandro Gattiker  1492.529478    19790917  19791119.0
4     Alejandro Gattiker  1485.415228    19791119         NaN
...                  ...          ...         ...         ...
8462       Tom Gullikson  1483.914545    19791029  19791105.0
8463       Tom Gullikson  1478.934755    19791105  19791105.0
8464       Tom Gullikson  1489.400521    19791105  19791105.0
8465       Tom Gullikson  1498.757080    19791105  19791113.0
8466       Tom Gullikson  1490.600009    19791113         NaN
```

- Once fit, we can transform a `DataFrame` or `ndarray` of player/player match data into the respective ratings for each player immediately *prior* to the match
```
>>> model.transform(df, output_type='rating')

               r1           r2
0     1598.787906  1530.008777
1     1548.633423  1585.653196
2     1548.633423  1598.787906
3     1445.555739  1489.089241
4     1439.595891  1502.254666
...           ...          ...
3954  1872.284295  1714.108269
3955  1872.284295  1698.007094
3956  1837.623245  1714.108269
3957  1837.623245  1698.007094
3958  1698.007094  1714.108269

[3959 rows x 2 columns]
```
- Alternatively, we could also transform a datafrom into the forecast probabilities of victory for the player `"winner_name"`:
```
>>> model.transform(df, output_type='prob')

0       0.597708
1       0.446925
2       0.428319
3       0.437676
4       0.410792
          ...
3954    0.713110
3955    0.731691
3956    0.670624
3957    0.690764
3958    0.476845
Length: 3959, dtype: float64
```

- These probabilities are also available using the `predict_proba` or `predict` classifier methods, as shown below. What distinguishes `transform` from `predict_proba` is that `predict_proba` and `predict` return predictions that only use past data (i.e. you cannot cheat by leaking future data into the forecast), while `transform(X, strict_past_data=False)` may be used to compute ratings that "peek" into the future and could return ratings updated using match outcomes pushed (slightly) back in time to the match start timestamp. This is a specific convenience utility for non-forecasting use cases in which the match start time is a more convenient timestamp with which to index and manipulate data.
```
>>> model.predict_proba(df)

           pr1       pr2
0     0.597708  0.402292
1     0.446925  0.553075
2     0.428319  0.571681
3     0.437676  0.562324
4     0.410792  0.589208
...        ...       ...
3954  0.713110  0.286890
3955  0.731691  0.268309
3956  0.670624  0.329376
3957  0.690764  0.309236
3958  0.476845  0.523155

[3959 rows x 2 columns]

>>> model.predict(df)

0       1.0
1       0.0
2       0.0
3       0.0
4       0.0
       ...
3954    1.0
3955    1.0
3956    1.0
3957    1.0
3958    0.0
Name: pr1, Length: 3959, dtype: float64
```

## Available Rating System Estimators

The models in this package are the following:

- [`EloEstimator`](https://github.com/mbhynes/skelo/blob/main/skelo/model/elo.py)
  - This class is a pure python implementation of a standard [Elo rating system](https://en.wikipedia.org/wiki/Elo_rating_system), without draws or homefield advantage
- [`Glicko2Estimator`](https://github.com/mbhynes/skelo/blob/main/skelo/model/glicko2.py)
  - This class implements the [Glicko2](https://en.wikipedia.org/wiki/Glicko_rating_system) rating system, implemented using a light wrapper around [glicko2](https://github.com/deepy/glicko2)

## Extended Usage Examples

### Synthetic Data

We can use a convenience utility for generating match data from players with normally distributed ratings.
The below example shows a simple usage of this utility:
```python
import pandas as pd
import matplotlib.pyplot as plt

import skelo.utils.elo_data as data_utils
from skelo.model.elo import EloEstimator

# Generate some sample constant ratings & match data
ratings = data_utils.generate_constant_ratings(num_players=10, sigma=500)
matches = pd.DataFrame(data_utils.generate_constant_game_outcomes(ratings, num_timesteps=100))

# Fit the model using numpy arrays
X = matches.values[:, :3] # player1, player2, match timestamp
y = matches.values[:, -1] # match outcome
model = EloEstimator().fit(X, y)

# Get a dataframe of the estimated ratings over time from the fitted model
ratings_est = model.rating_model.to_frame()

# Compare the ratings estimate over time
ts_est = ratings_est.pivot_table(index='valid_from', columns='key', values='rating')
ts_est.plot()
```
![Convergence of Synthetic Ratings](https://raw.githubusercontent.com/mbhynes/skelo/main/examples/ratings_convergence.png)

The estimated ratings will exhibit convergence profiles (players with extremal low or high ratings take longer to converge).
Please note that while the actual original ratings are unlikely to be determined by the fitting procedure, the *relative* difference between the ratings should be preserved, within the noise band of the chosen value of `k` (by default: 20)

### Example Tennis Ranking

```python
import numpy as np
import pandas as pd
from skelo.model.elo import EloEstimator
from sklearn.metrics import precision_score

# Download a dataframe of example tennis data from JeffSackmann's ATP match repository (thanks Jeff!)
def load_data():
  df = pd.concat([
    pd.read_csv("https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_1979.csv"),
    pd.read_csv("https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_1980.csv"),
  ], axis=0)
  # Do some simple munging to get a date and a match outcome label
  df["tourney_date"] = pd.to_datetime(df['tourney_date'], format='%Y%m%d')

  # For measuring classifier calibration linearity, it's better to have both true and false
  # labels in the dataset, so we relabel the order of (winner, loser) to just be (player1, player2)
  order_mask = (df["winner_name"] < df["loser_name"])
  df["p1"] = ""
  df["p2"] = ""
  df["label"] = 1
  df.loc[order_mask, "p1"] = df.loc[order_mask, "winner_name"]
  df.loc[~order_mask, "p1"] = df.loc[~order_mask, "loser_name"]
  df.loc[order_mask, "p2"] = df.loc[order_mask, "loser_name"]
  df.loc[~order_mask, "p2"] = df.loc[~order_mask, "winner_name"]
  df.loc[~order_mask, "label"] = 0
  return df

df = load_data()
player_counts = pd.concat([df["p1"], df["p2"]], axis=0).value_counts()
players = player_counts[player_counts > 5].index
mask = (df["p1"].isin(players) & df["p2"].isin(players))
X = df.loc[mask]

# Create a model to fit on a dataframe.
# Since our match data has np.datetime64 timestamps, we specify an initial time explicitly
model = EloEstimator(
  key1_field="p1",
  key2_field="p2",
  timestamp_field="tourney_date",
  initial_time=np.datetime64('1979', 'Y'),
).fit(X, X["label"])

#  Retrieve the fitted Elo ratings from the model
ratings_est = model.rating_model.to_frame()
ts_est = ratings_est.pivot_table(index='valid_from', columns='key', values='rating').ffill()

idx = ts_est.iloc[-1].sort_values().index[-5:]
ts_est.loc[:, idx].plot()
```

This should result in a figure like the one below, showing the 5 highest ranked (within the Elo system) players based on this subset of ATP matches:
![Top ATP Player Ratings, 1979-1980](https://raw.githubusercontent.com/mbhynes/skelo/main/examples/atp_1979.png)

### Example Tennis Ranking - Elo ratings using the `sklearn` API

While the ratings are interesting to visualize, the accuracy of the rating system's predictions are more important.
For determining the performance of a classifier, the `sklearn` API and model utilities provide simple tools for us.
Below we calculate the classification metrics of the Elo system using only the 1980 data, where each prediction for a match uses only the outcomes of previous matches:

```python
>>> from sklearn.metrics import classification_report
>>> mask = (X["tourney_date"] > "1980-01-01")
>>> print(classification_report(X.loc[mask, "label"], model.predict(X.loc[mask])))

              precision    recall  f1-score   support

           0      0.692     0.704     0.698      1782
           1      0.700     0.689     0.694      1792

    accuracy                          0.696      3574
   macro avg      0.696     0.696     0.696      3574
weighted avg      0.696     0.696     0.696      3574
```

Finally, we should inspect the calibration curve of the classifier to verify its linearity (or lack thereof!):
```
import matplotlib.pyplot as plt
from sklearn.calibration import calibration_curve

prob_true, prob_pred = calibration_curve(
  X.loc[mask, "label"],
  model.predict_proba(X.loc[mask]).values[:, 0],
  n_bins=10
)
plt.plot(prob_pred, prob_true, label=f"Elo Classifier, k={model.rating_model.default_k}", marker='s', color='b')
plt.plot([0, 1], [0, 1], label="Perfect Calibration", ls=":", color='k')
plt.xlabel("Predicted Probability")
plt.ylabel("Empirical Probability")
plt.legend()
```

It's interesting to note that the calibration curve is not strictly linear, but rather has a slight but noticeable sigmoidal shape. If you plan on doing anything with the Elo predictions in aggregate, you may want to consider [calibrating the classifier output](https://scikit-learn.org/stable/modules/calibration.html).

![Elo Calibration for ATP Matches, 1980](https://raw.githubusercontent.com/mbhynes/skelo/main/examples/atp_1979-calibration.png)

### Example Paramter Tuning

We can now also use the `sklearn` utilities for parameter tuning, similar to any other forecasting model.
The below example trains several instances of the Elo ratings model with different values of `k` to find value that maximizes prediction accuracy during the ATP matches for 1980.
Please note that the `EloModel.predict()` method only uses past information available at each match, such that there is no leakage of information from the future in the model's forecasts.
```python
from sklearn.model_selection import GridSearchCV

clf = GridSearchCV(
  model,
  param_grid={
    'default_k': [
      10, 15, 20, 25, 30, 35, 40, 45, 50,
    ]
  },
  cv=[(X.index, X.index[len(X)//2:])],
).fit(X, X['label'])

results = pd.DataFrame(clf.cv_results_)
```

This should produce a result like the following:
```python
>>> results.sort_values('rank_test_score').head(5).T

                                   2                  4                  1                  6                  8
mean_fit_time               0.193156           0.172773           0.490719           0.162793           0.045234
std_fit_time                     0.0                0.0                0.0                0.0                0.0
mean_score_time             0.269005           0.304881           0.336428           0.288607            0.06117
std_score_time                   0.0                0.0                0.0                0.0                0.0
param_default_k                   20                 30                 15                 40                 50
params             {'default_k': 20}  {'default_k': 30}  {'default_k': 15}  {'default_k': 40}  {'default_k': 50}
split0_test_score           0.678779           0.677115            0.67656           0.675451           0.675451
mean_test_score             0.678779           0.677115            0.67656           0.675451           0.675451
std_test_score                   0.0                0.0                0.0                0.0                0.0
rank_test_score                    1                  2                  3                  4                  4
```

### Example Tennis Ranking - Glicko2 ratings using the `sklearn` API

The Glicko2 implementation that we wrap has no hyperparameters to tune other than the 3-tuple to provide as the `initial_value` for a player; namely the initial values of the rating, the rating deviation, and the volatility.
(However the persnickety reader should note there are quite a lot of magic numbers in both the [Glicko](http://www.glicko.net/glicko/glicko.pdf) and [Glicko2](http://www.glicko.net/glicko/glicko2.pdf) papers that should probably constitute hyperparameters---these aren't available to tune simply because the available Glicko{1,2} implementations do not expose an interface that allows for tuning them. Beggars can't be choosers, as they say...)

```python
import numpy as np
from skelo.model.glicko2 import Glicko2Estimator
from sklearn.model_selection import GridSearchCV

model = Glicko2Estimator(
  key1_field="p1",
  key2_field="p2",
  timestamp_field="tourney_date",
  initial_time=np.datetime64('1979', 'Y'),
)

clf = GridSearchCV(
  model,
  param_grid={
    'initial_value': [
      (1500., 200., 0.06),
      (1500., 350., 0.06),
      (1500., 500., 0.06),
      (1500., 750., 0.06),
    ]
  },
  cv=[(X.index, X.index[len(X)//2:])],
).fit(X, X['label'])

results = pd.DataFrame(clf.cv_results_)
```

We can now compare the best Glicko2 models with the Elo above and note that our test period forecasting accuracy has improved from 67.8% to 69.3%:
```python
>>> results.sort_values('rank_test_score').head(2).T

                                                            3                                         2
mean_fit_time                                        0.212466                                  0.217531
std_fit_time                                              0.0                                       0.0
mean_score_time                                      0.068816                                  0.083714
std_score_time                                            0.0                                       0.0
param_initial_value                     (1500.0, 750.0, 0.06)                     (1500.0, 500.0, 0.06)
params               {'initial_value': (1500.0, 750.0, 0.06)}  {'initial_value': (1500.0, 500.0, 0.06)}
split0_test_score                                    0.693204                                  0.692649
mean_test_score                                      0.693204                                  0.692649
std_test_score                                            0.0                                       0.0
rank_test_score                                             1                                         2
```


## Development Setup

If you would like to contribute to this repository, please open an [issue](issues/new) first to document the extension or modification you're interested in contributing.

### `dev` scripts

The `dev` script (and other scripts in `bin`) contain convenience wrappers for some common development tasks:

- **Installing Dependencies**. To set up a local development environment with dependencies, use:
  ```bash
  ./dev up
  ```
  This will create a `.venv` directory in the project root and install the required python packages.

- **Running Tests**. To run the project tests with the virtual environment's `pytest`, use:

  ```python
  ./dev test
  ```
  Arguments to `pytest` can be supplied to this script as well, to e.g. limit the tests to a particular subset.

- **Packaging for PyPI**. The package-building and upload process is wrapped with the following commands:

  ```python
  ./dev package
  ./dev upload --test 
  ./dev upload
  ```

### Creating an `RatingEstimator`

The available models extend the `skelo.model.RatingEstimator` [class](https://github.com/mbhynes/skelo/blob/main/skelo/model/__init__.py) which implements the `sklearn` wrapper interface around a `skelo.model.RatingModel` instance.

To create a new classifier, it's necessary to:
  - Extend the `RatingModel` to implement the rating update formulas through the methods:
    - `evolve_rating(r1, r2, label)`, which computes a new rating given the players' previous ratings, `r1` and `r2`, prior to a match with outcome `label`
    - `compute_prob(r1, r2)`, which computes the probability of victory of a player with rating `r1` over a player with rating `r2`

```python
class EloModel(RatingModel):
  def __init__(self, default_k=20, k_fn=None, initial_value=1500, initial_time=0, **kwargs):
    super().__init__(initial_value=float(initial_value), initial_time=initial_time)
    # Set all hyperparameters as explicit attributes, such that sklearn's CV utilities work
    self.default_k = default_k
    self.k = k_fn or (lambda _: default_k)

  def evolve_rating(self, r1, r2, label):
    exp = self.compute_prob(r1, r2)
    return r1 + self.k(r1) * (label - exp)

  @staticmethod
  def compute_prob(r1, r2):
    diff = (r2 - r1) / 400.0
    prob = 1.0 / (1 + 10**diff)
    return prob
```

  - Extend the `RatingEstimator` to wrap the new `RatingModel` subclass and specify the list of `RatingModel` attributes that should be considered hyperparamters when dynamically building a `RatingsModel` in the estimator's `fit` method

```python
class EloEstimator(RatingEstimator):

  # Set this to the appropriate RatingModel:
  RATING_MODEL_CLS = EloModel

  # Include all tunable hyperparameter attributes of the RatingModel here:
  RATING_MODEL_ATTRIBUTES = [
    'default_k',
    'k_fn',
    'initial_value',
    'initial_time',
  ]

  def __init__(self, key1_field=None, key2_field=None, timestamp_field=None, default_k=20, k_fn=None, initial_value=1500, initial_time=0, **kwargs):
    super().__init__(
      key1_field=key1_field,
      key2_field=key2_field,
      timestamp_field=timestamp_field,
      initial_value=initial_value,
      initial_time=initial_time,
      **kwargs
    )
    # These must be set as estimator attributes
    self.default_k = default_k
    self.k_fn = k_fn
```

Please note that a `rating` can be anything, so long as it's convenient and can support the above call signatures to create a new rating object like-for-like from existing rating objects.
For example, our `EloModel` implementation uses a plain `float`, and the `Glicko2Model` uses a 3-tuple for the 3 generative parameters for a player's rating.