跨行查找关系的 ML 模型

Posted 2023-03-11

【中文标题】跨行查找关系的 ML 模型

【英文标题】：ML model that finds relationships across the rows

【发布时间】：2021-10-26 11:59:35

【问题描述】：

我从网球比赛video 中提取了一个球的 x,y 坐标 附言x,y坐标格式如下

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(1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0), (1163.0, 523.0)]

使用这些坐标，我构建了一个由 3 列组成的 DataFrame：x、y、bounce（球接触球场的坐标 - 手动找到）

示例：

...	x	y	bounce
0	985.0	325.0	0
1	985.0	325.0	0
2	985.0	325.0	0
3	985.0	325.0	0
4	985.0	325.0	0
..	...	...	...
921	1163.0	523.0	0
922	1163.0	523.0	0
923	1163.0	523.0	0
924	1163.0	523.0	0
925	1275.0	572.0	1
[926 rows x 3 columns]

如果我根据帧号绘制 x 和 y 坐标，并为反弹点绘制散点图，我得到

我可以看到存在某种模式，反弹发生在峰值前 10-30 帧（局部 Max/local Min），但我使用 sklearn 构建的 ML 模型似乎无法捕捉到它。

import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix

X = df.drop('bounce', 1)
y = df['bounce']

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)

grid="C":np.logspace(-3,3,7), "penalty":["l1","l2"]# l1 lasso l2 ridge
logreg = LogisticRegression(C = 0.001, penalty='l2')
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)

tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
print(tn, fp, fn, tp)
>>> 381 0 6 0

据我所知，对于 sklearn ML 模型而言，它会寻找跨列的关系，而每一行都是一个新案例。

在我的情况下，列之间没有太多，而是行具有更高的价值，我该如何相应地训练我的模型？

【参考方案1】：

将帧视为时间点，将数据视为时间序列数据将有助于更好地捕捉关系。时间序列模型有不同的训练方法，最好相应地重新构建预测问题。

This Post 似乎是这个问题的一个很好的起点。