跨行查找关系的 ML 模型

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【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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(961.0, 364.0), (961.0, 364.0), (963.0, 363.0), (963.0, 364.0), (962.0, 364.0), (965.0, 365.0), (967.0, 367.0), (967.0, 367.0), (967.0, 367.0), (966.0, 370.0), (966.0, 371.0), (968.0, 374.0), (969.0, 377.0), (969.0, 377.0), (969.0, 381.0), (969.0, 384.0), (969.0, 388.0), (970.5, 391.0), (972.0, 394.0), (972.0, 394.0), (971.0, 399.0), (972.0, 404.0), (973.0, 409.0), (972.0, 414.0), (973.0, 418.0), (973.0, 418.0), (973.0, 424.0), (973.0, 430.0), (972.0, 437.0), (972.1, 436.3), (972.2, 435.6), (972.3, 434.9), (972.4, 434.2), (972.5, 433.5), (972.6, 432.8), (972.7, 432.1), (972.8, 431.4), (972.9, 430.7), (973.0, 430.0), (974.0, 423.0), (974.0, 416.0), (974.0, 411.0), (974.0, 407.0), (974.0, 407.0), (973.0, 403.0), (972.0, 396.0), (971.5, 392.5), (971.0, 389.0), (973.0, 385.0), (973.0, 385.0), (972.0, 384.0), (973.0, 379.0), (972.0, 377.0), (971.0, 374.0), (972.0, 373.0), (973.0, 373.0), (972.0, 372.9375), (971.0, 372.875), (970.0, 372.8125), (969.0, 372.75), (968.0, 372.6875), (967.0, 372.625), (966.0, 372.5625), (965.0, 372.5), (964.0, 372.4375), (963.0, 372.375), (962.0, 372.3125), (961.0, 372.25), (960.0, 372.1875), (959.0, 372.125), (958.0, 372.0625), (957.0, 372.0), (956.3529411764706, 374.3529411764706), (955.7058823529412, 376.70588235294116), (955.0588235294117, 379.05882352941177), (954.4117647058823, 381.4117647058824), (953.7647058823529, 383.7647058823529), (953.1176470588235, 386.11764705882354), (952.4705882352941, 388.47058823529414), (951.8235294117648, 390.8235294117647), (951.1764705882352, 393.1764705882353), (950.5294117647059, 395.52941176470586), (949.8823529411765, 397.88235294117646), (949.2352941176471, 400.2352941176471), (948.5882352941177, 402.5882352941176), (947.9411764705883, 404.94117647058823), (947.2941176470588, 407.29411764705884), (946.6470588235294, 409.6470588235294), (946.0, 412.0), (941.0, 413.0), (935.0, 414.0), (936.0, 414.0), (929.0, 414.0), (922.0, 413.0), (914.0, 414.0), (908.0, 416.0), (902.0, 417.0), (902.0, 417.0), (894.0, 418.0), (889.0, 422.0), (882.0, 424.0), (873.0, 425.0), (867.0, 428.0), (867.0, 428.0), (861.0, 436.0), (853.0, 437.0), (846.8888888888889, 442.5), (840.7777777777778, 448.0), (834.6666666666666, 453.5), (828.5555555555555, 459.0), (822.4444444444445, 464.5), (816.3333333333334, 470.0), (810.2222222222222, 475.5), (804.1111111111111, 481.0), (798.0, 486.5), (791.8888888888889, 492.0), (785.7777777777778, 497.5), (779.6666666666666, 503.0), (773.5555555555555, 508.5), (767.4444444444445, 514.0), (761.3333333333334, 519.5), (755.2222222222222, 525.0), (749.1111111111111, 530.5), (743.0, 536.0), (738.0, 543.0), (732.0, 552.0), (732.0, 545.0), (732.0, 545.0), (734.0, 532.0), (723.0, 549.0), (712.0, 566.0), (701.0, 583.0), (711.8333333333334, 556.6666666666666), (722.6666666666666, 530.3333333333334), (733.5, 504.0), (744.3333333333334, 477.6666666666667), (755.1666666666666, 451.33333333333337), (766.0, 425.0), (770.0, 413.0), (769.0, 414.0), (774.0, 405.0), (776.0, 392.0), (780.0, 384.0), (783.0, 374.0), (788.0, 363.0), (788.0, 363.0), (791.0, 354.0), (794.0, 346.0), (799.0, 339.0), (802.0, 331.0), (805.0, 324.0), (805.0, 324.0), (808.0, 319.0), (813.0, 312.0), (814.0, 304.0), (818.0, 299.0), (824.0, 295.0), (824.0, 295.0), (828.0, 288.0), (828.0, 285.0), (832.0, 280.0), (835.0, 276.0), (838.0, 274.0), (838.0, 274.0), (842.0, 269.0), (846.0, 266.0), (848.0, 265.0), (852.0, 262.0), (855.0, 260.0), (855.0, 260.0), (857.0, 259.0), (861.0, 256.0), (864.5, 254.5), (868.0, 253.0), (885.75, 281.4166666666667), (903.5, 309.8333333333333), (921.25, 338.25), (939.0, 366.6666666666667), (956.75, 395.08333333333337), (974.5, 423.5), (992.25, 451.9166666666667), (1010.0, 480.33333333333337), (1027.75, 508.75), (1045.5, 537.1666666666667), (1063.25, 565.5833333333334), (1081.0, 594.0), (1091.0, 586.0), (1091.0, 586.0), (1103.0, 573.0), (1113.0, 564.0), (1126.0, 553.0), (1140.0, 542.0), (1150.0, 533.0), (1150.0, 533.0), (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), (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?

【问题讨论】:

【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。

【讨论】:

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