frc-analytics/analyzer.py at master · richzli/frc-analytics · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
"""
various functions that make data into actual stats
"""

import pandas as pd
import numpy as np
from scipy import linalg as ela
import os
import accessor

def compile_teams(year, eventcode):
    eventcode = eventcode.upper()

    relevant_stat_endings = ("Points", "Point", "Count")
    matches_headers = ["matchNumber", "blue1", "blue2", "blue3", "red1", "red2", "red3"]

    data_matches, data_scores_qual, data_scores_playoff = accessor.fetch_matches(year, eventcode)
    all_stats = list(data_scores_qual[0]["alliances"][0].keys())
    relevant_stats = []
    for s in all_stats:
        if s.endswith(relevant_stat_endings):
            relevant_stats.append(s)

    data_matches_array = []

    for game in data_matches:
        matchNumber = game["tournamentLevel"]+str(game["matchNumber"])
        row = {"matchNumber": matchNumber}
        for team in game["teams"]:
            row[team["station"].lower()] = team["teamNumber"]
        data_matches_array.append([row[s] for s in matches_headers])

    score_headers = ["matchNumber"] + ["blue"+s for s in relevant_stats] + \
                  ["red"+s for s in relevant_stats]
    score_stats_array = []

    for game in data_scores_qual:
        matchNumber = game["matchLevel"]+str(game["matchNumber"])
        row = {"matchNumber": matchNumber}
        for team in game["alliances"]:
            for stat in relevant_stats:
                row[team["alliance"].lower()+stat] = int(team[stat])
        score_stats_array.append([row[s] for s in score_headers])

    """
    for game in data_scores_playoff:
        matchNumber = game["matchLevel"]+str(game["matchNumber"])
        row = {"matchNumber": matchNumber}
        for team in game["alliances"]:
            for stat in relevant_stats:
                row[team["alliance"].lower()+stat] = int(team[stat])
        score_stats_array.append([row[s] for s in score_headers])
    """

    matches_df = pd.DataFrame(data_matches_array, columns=matches_headers)
    scores_df = pd.DataFrame(score_stats_array, columns=score_headers)

    if not os.path.exists("./data/raw"):
        os.mkdir("./data/raw")

    compiled = pd.concat([matches_df.set_index("matchNumber"),
                          scores_df.set_index("matchNumber")], axis=1,
                         join="inner").reset_index()
    compiled.to_csv("./data/raw/" + year + eventcode + ".csv", index=False)

def get_team_numbers(teams):
    nums = []
    for team in teams["teams"]:
        nums.append(team["teamNumber"])
    return nums

def calculate_ratings(year, eventcode):
    eventcode = eventcode.upper()

    teamnums = get_team_numbers(accessor.fetch_teams(year, eventcode))
    teamnparray = np.array([teamnums])
    teamarray = []
    statarray = []

    data = accessor.csv_to_2darray("./data/raw/" + year + eventcode + ".csv")
    colnames = data.pop(0)
    statnames = colnames[7:]
    statnames = ["teamNum"] + [col[4:] + "_OPR" \
                 if col.startswith("blue") else \
                 col[3:] + "_DPR" for col in statnames]

    for row in data:
        matchnumber = row[0]
        blueteams = row[1:4]
        blueteams = [int(t) for t in blueteams]
        redteams = row[4:7]
        redteams = [int(t) for t in redteams]
        stats = row[7:]
        bluestats = stats[:len(stats)//2]
        redstats = stats[len(stats)//2:]

        teamarray.append([1 if team in blueteams else 0 for team in teamnums])
        teamarray.append([1 if team in redteams else 0 for team in teamnums])
        #i love python list comprehension

        statarray.append(bluestats+redstats)
        statarray.append(redstats+bluestats)

    teammatrix = np.array(teamarray, dtype=int)
    statmatrix = np.array(statarray, dtype=float)

    ratings = do_math(teammatrix, statmatrix)
    ratings = np.concatenate((teamnparray.T, ratings), axis=1)

    if not os.path.exists("./data/processed"):
        os.mkdir("./data/processed")

    pd.DataFrame(ratings).to_csv("./data/processed/" + year + eventcode + ".csv",
                                 header=statnames, index=None)

    #return ratings

def do_math(teams, stats):
    L = ela.cholesky((teams.T @ teams), lower = True, check_finite = False)
    ATb = (teams.T @ stats)
    y = ela.solve_triangular(L, ATb, lower = True, check_finite = False)
    x = ela.solve_triangular(L.T, y, check_finite = False)

    return x