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added synthetic data support

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This commit is contained in:
Sandro Zimmermann 2025-11-29 14:33:03 +01:00
parent 0d978b9463
commit 3bcbbdf544
8 changed files with 2311 additions and 7 deletions
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133
absolut.py Normal file
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@ -0,0 +1,133 @@
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder
FEATURES = ["points", "x", "y"]
# create dataframe from csv and drop any row with null values
def load_dataframe():
try:
colum_list = FEATURES
df = pd.read_csv("data/shots.csv", usecols = colum_list).dropna()
return df.abs()
except FileNotFoundError as error:
print(error)
quit()
def calc_f1_macro(y_true, y_pred):
f1_scores = []
for column in y_true:
score = calc_f1_score(y_true[column].values, y_pred[column])
f1_scores.append(score)
return np.mean(f1_scores)
def calc_f1_score(y_true, y_pred):
tp = np.sum(np.multiply([i==True for i in y_pred], y_true))
tn = np.sum(np.multiply([i==False for i in y_pred], [not(j) for j in y_true]))
fp = np.sum(np.multiply([i==True for i in y_pred], [not(j) for j in y_true]))
fn = np.sum(np.multiply([i==False for i in y_pred], y_true))
'''print(tp)
print(fp)
precision = calc_precision(tp, fp)
recall = calc_recall(tp, fn)'''
if tp != 0 and fp != 0:
precision = calc_precision(tp, fp)
else:
precision = 0
if tp != 0 and fn != 0:
recall = calc_recall(tp, fn)
else:
recall = 0
if precision != 0 and recall != 0:
f1 = (2 * precision * recall) / (precision + recall)
else:
f1 = 0
return f1
def calc_precision(tp, fp):
return tp / (tp + fp)
def calc_recall(tp, fn):
return tp / (tp + fn)
def get_score_from_cli():
try:
x = float(input("x: "))
y = float(input("y: "))
return np.array([x, y]).reshape(1, -1)
except ValueError:
print("Invalid input. Please enter numeric values.")
return None
def main():
df = load_dataframe()
print(df.describe())
print(df.head())
print(df.head().info())
sns.countplot(x = df["points"])
plt.show()
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.show()
sns.scatterplot(x=df['x'], y=df['y'], hue=df['points'])
plt.show()
quit()
features = ["x", "y"]
X = df[features]
y = pd.get_dummies(df['points'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
random_forest = RandomForestClassifier(n_estimators=700, random_state=0)
decision_tree = DecisionTreeClassifier(random_state=0)
k_neighbors = KNeighborsClassifier(n_neighbors=5)
models = {
"Random Forest Classifier": random_forest,
"Decision Tree Classifier": decision_tree,
"K-Neighbors": k_neighbors
}
for name, model in models.items():
model.fit(X_train.values, y_train.values)
for name, model in models.items():
pred = model.predict(X_test.values)
my_f1_macro_score = calc_f1_macro(y_test, pd.DataFrame(pred))
print(f'My F1 score of {name} is {my_f1_macro_score}\n')
f1_sklearn = f1_score(y_test.values, pred, average='macro')
print(f'Sklearn F1 score of {name} is {f1_sklearn}\n')
score = get_score_from_cli()
label_encoder = LabelEncoder()
df["points"] = label_encoder.fit_transform(df["points"])
for name, model in models.items():
pred = model.predict(score)
points_number = pd.DataFrame(pred).idxmax(axis=1)
points = label_encoder.inverse_transform(points_number)[0]
print(f"{name}: {points} Punkte")
if __name__ == "__main__":
main()

2
data/shots.csv
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@ -780,7 +780,7 @@ points,x,y
10,0.0,0.0 10,0.0,0.0
6,4.0,-3.0 6,4.0,-3.0
10,-1.0,-1.0 10,-1.0,-1.0
9,6.0,-2.0 9,1.5,-2.0
8,1.5,-2.5 8,1.5,-2.5
6,-1.0,-4.5 6,-1.0,-4.5
6,5.5,-1.5 6,5.5,-1.5

1 points x y
780 10 0.0 0.0
781 6 4.0 -3.0
782 10 -1.0 -1.0
783 9 6.0 1.5 -2.0
784 8 1.5 -2.5
785 6 -1.0 -4.5
786 6 5.5 -1.5

955
data/shots_dev.csv Normal file
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@ -0,0 +1,955 @@
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4,7.0,1.5
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1 points x y
2 7 -2.5 -3.0
3 0 -11.0 -4.0
4 9 1.5 2.0
5 6 -3.5 4.5
6 10 0.5 -1.0
7 4 -6.5 1.0
8 8 2.5 1.5
9 0 12.5 6.5
10 10 0.0 -0.5
11 6 -4.5 3.5
12 7 -3.0 -3.5
13 6 5.0 0.0
14 0 -10.5 -4.0
15 7 -3.5 1.5
16 0 -10.5 -1.0
17 4 -1.0 -7.0
18 8 -1.0 2.5
19 8 2.5 -1.5
20 7 -1.5 4.0
21 6 -4.0 -3.0
22 6 -4.5 -1.0
23 7 4.0 -0.5
24 8 0.0 2.5
25 4 6.5 4.0
26 8 2.5 -1.5
27 7 -2.5 -2.5
28 7 -1.0 3.5
29 6 4.5 2.5
30 7 -4.0 0.0
31 7 -4.0 -2.5
32 9 2.5 -1.5
33 7 1.5 4.0
34 6 -5.0 -2.0
35 7 -3.0 -2.0
36 6 5.0 2.0
37 2 8.5 -3.5
38 8 -2.0 2.5
39 10 -0.5 -1.5
40 4 -7.5 1.0
41 8 -3.0 -0.5
42 8 -1.0 2.5
43 8 1.0 -3.0
44 10 -1.0 0.5
45 10 -1.0 0.5
46 9 -1.5 2.0
47 7 3.0 3.5
48 9 0.0 2.0
49 9 -1.5 2.0
50 9 -1.0 -1.5
51 6 4.5 -1.5
52 6 4.5 3.0
53 1 10.0 2.5
54 5 3.5 4.5
55 8 3.0 -1.5
56 9 1.5 0.5
57 0 10.5 8.0
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59 8 2.5 -2.0
60 3 -8.0 -3.5
61 4 7.0 2.0
62 8 1.5 2.5
63 6 3.5 4.0
64 4 -6.5 -3.5
65 5 4.0 5.0
66 8 1.5 3.0
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93 8 3.5 1.0
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101 5 -5.5 -2.5
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104 4 2.0 6.5
105 7 -1.5 4.0
106 7 -2.5 3.0
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111 4 3.5 -6.0
112 5 4.0 5.5
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114 5 3.0 6.0
115 5 -1.5 -5.5
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117 5 -5.5 0.5
118 7 0.5 -4.0
119 3 -1.0 -7.5
120 10 -0.5 0.0
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123 7 3.5 1.5
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125 5 -5.0 -3.0
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127 6 2.5 4.5
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140 7 -3.5 -2.0
141 8 -2.5 -0.5
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144 5 -5.5 3.5
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153 8 -1.5 -3.0
154 4 7.0 1.0
155 0 -16.0 -2.0
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159 7 3.5 -2.5
160 5 1.5 -5.5
161 0 8.5 9.0
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163 7 -1.5 -4.0
164 7 -3.5 1.5
165 7 -2.0 -3.5
166 8 0.5 3.5
167 6 3.0 4.5
168 8 2.0 2.5
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170 7 3.5 1.0
171 6 4.5 0.5
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175 8 -3.0 -1.0
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182 6 5.0 2.5
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184 10 1.5 1.5
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187 6 -4.5 1.0
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189 7 4.0 1.5
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214 8 1.5 2.5
215 6 4.0 4.5
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220 8 -0.5 2.5
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222 8 2.5 2.0
223 6 -5.0 -1.0
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225 9 -1.0 1.5
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227 7 -3.5 2.0
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231 8 -1.5 -3.0
232 9 -1.5 2.0
233 6 -4.5 2.5
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235 8 -2.5 -2.0
236 8 -2.5 -1.5
237 6 -4.0 -2.5
238 5 -6.0 -1.5
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240 8 1.5 2.5
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242 7 -3.5 2.0
243 8 3.0 1.5
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245 10 -1.5 -1.0
246 6 2.5 4.0
247 8 2.5 1.5
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250 7 -3.5 -2.0
251 5 5.5 0.0
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253 6 -5.0 0.5
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255 7 -3.5 -1.0
256 6 2.5 4.5
257 3 -4.5 -6.5
258 9 -1.5 -1.0
259 9 0.0 -2.0
260 7 -3.5 2.5
261 5 -5.0 -3.0
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265 8 2.5 -2.0
266 9 1.5 1.5
267 8 3.0 1.5
268 10 -1.5 0.0
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270 8 2.5 1.0
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272 6 -1.0 -4.5
273 7 -2.0 -4.0
274 9 -1.5 -2.0
275 4 -7.0 -1.0
276 9 1.5 1.5
277 9 -2.0 -1.5
278 6 -4.0 3.0
279 10 0.5 0.5
280 7 -3.0 3.5
281 5 -5.5 2.5
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283 4 -6.0 5.5
284 9 1.0 2.0
285 9 2.5 1.5
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288 8 -1.5 2.5
289 2 -8.0 4.0
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291 9 1.5 1.0
292 7 2.5 3.0
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305 7 -3.5 -3.0
306 7 -3.5 1.0
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311 5 4.0 5.0
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314 8 1.5 3.0
315 7 4.5 1.5
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322 8 2.5 2.5
323 9 1.5 -1.5
324 9 1.5 1.5
325 9 1.5 1.5
326 7 -3.5 -1.0
327 4 -3.0 -6.5
328 10 -1.0 -1.0
329 7 -2.5 3.5
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332 9 -1.5 0.0
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335 8 1.5 -2.5
336 8 0.0 -2.5
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340 9 0.0 -1.5
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347 9 0.0 2.0
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349 10 0.5 1.0
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351 7 2.0 3.5
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353 8 -2.5 1.5
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356 5 3.0 5.5
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359 7 3.5 0.0
360 7 -3.5 3.0
361 6 2.5 -4.5
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363 7 -3.5 2.5
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366 8 -2.5 -2.5
367 7 -1.5 3.5
368 9 -1.5 0.5
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data/shots_dev.xlsx Normal file
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1001
data/synthetic_data.csv Normal file
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File diff suppressed because it is too large Load Diff

15
main.py
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@ -15,7 +15,8 @@ FEATURES = ["points", "x", "y"]
def load_dataframe(): def load_dataframe():
try: try:
colum_list = FEATURES colum_list = FEATURES
df = pd.read_csv("data/shots.csv", usecols = colum_list).dropna() #df = pd.read_csv("data/shots.csv", usecols = colum_list).dropna()
df = pd.read_csv("data/synthetic_data.csv", usecols = colum_list).dropna()
return df return df
except FileNotFoundError as error: except FileNotFoundError as error:
print(error) print(error)
@ -73,8 +74,9 @@ def get_score_from_cli():
def main(): def main():
df = load_dataframe() df = load_dataframe()
'''print(df.head()) print(df.describe())
#print(df.head())
#print(df.head().info())
sns.countplot(x = df["points"]) sns.countplot(x = df["points"])
plt.show() plt.show()
@ -82,10 +84,11 @@ def main():
plt.show() plt.show()
sns.scatterplot(x=df['x'], y=df['y'], hue=df['points']) sns.scatterplot(x=df['x'], y=df['y'], hue=df['points'])
plt.show()''' plt.show()
df["radius"] = np.sqrt(df["x"]**2 + df["y"]**2)
X = df[["radius"]] features = ["x", "y"]
X = df[features]
y = pd.get_dummies(df['points']) y = pd.get_dummies(df['points'])

85
rule_based_values.py Normal file
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@ -0,0 +1,85 @@
# generate points and x, y coordinates based on this rules
# 1. points possible: 0 to 10
# 2. possible points for x, y range
# 2a. 10 (0,0) (1.5, 1.5)
# 2.b 9 (1.5,0) (1.5, 1.5)
# 2.c 8 (0,0) (1.5, 1.5)
# 2.d 7 (0,0) (1.5, 1.5)
# 2.e 6 (0,0) (1.5, 1.5)
# 2.f 5 (0,0) (1.5, 1.5)
# 2.g 4 (0,0) (1.5, 1.5)
# 2.h 3 (0,0) (1.5, 1.5)
# 2.i 2 (0,0) (1.5, 1.5)
# 2.j 1 (0,0) (1.5, 1.5)
# 2.j 0 (0,0) (1.5, 1.5)
import pandas as pd
import numpy as np
import csv
FEATURES = ["points", "x", "y"]
# Area circle
A10 = 1.5 ** 2 * np.pi
A9 = 2.5 ** 2 * np.pi
A8 = 3.5 ** 2 * np.pi
A7 = 4.5 ** 2 * np.pi
A6 = 5.5 ** 2 * np.pi
A5 = 6.5 ** 2 * np.pi
A4 = 7.5 ** 2 * np.pi
A3 = 8.5 ** 2 * np.pi
A2 = 9.5 ** 2 * np.pi
A1 = 10.5 ** 2 * np.pi
'''print(A10)
print(A9)
print(A8)
print(A7)
print(A6)
print(A5)
print(A4)
print(A3)
print(A2)
print(A1)
print("")'''
possible_values = np.linspace(-10, 10, 41) # frin -10 to 10 with step 0.5
xy = [(np.random.choice(possible_values), np.random.choice(possible_values)) for _ in range(1000)]
dataset = []
for i in xy:
A = (i[0]**2 + i[1]**2) * np.pi
#print(A)
if A <= A10:
dataset.append([10, i[0], i[1]])
elif A > A10 and A <= A9:
dataset.append([9, i[0], i[1]])
elif A > A9 and A <= A8:
dataset.append([8, i[0], i[1]])
elif A > A8 and A <= A7:
dataset.append([7, i[0], i[1]])
elif A > A7 and A <= A6:
dataset.append([6, i[0], i[1]])
elif A > A6 and A <= A5:
dataset.append([5, i[0], i[1]])
elif A > A5 and A <= A4:
dataset.append([4, i[0], i[1]])
elif A > A4 and A <= A3:
dataset.append([3, i[0], i[1]])
elif A > A3 and A <= A2:
dataset.append([2, i[0], i[1]])
elif A > A2 and A <= A1:
dataset.append([1, i[0], i[1]])
elif A > A1:
dataset.append([0, i[0], i[1]])
#print(dataset)
with open('data/synthetic_data.csv', 'w', newline='') as csvfile:
fieldnames = ['points', 'x', 'y']
writer = csv.writer(csvfile)
writer.writerows(dataset)

127
vector.py Normal file
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@ -0,0 +1,127 @@
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder
FEATURES = ["points", "x", "y"]
# create dataframe from csv and drop any row with null values
def load_dataframe():
try:
colum_list = FEATURES
df = pd.read_csv("data/synthetic_data.csv", usecols = colum_list).dropna()
return df
except FileNotFoundError as error:
print(error)
quit()
def calc_f1_macro(y_true, y_pred):
f1_scores = []
for column in y_true:
score = calc_f1_score(y_true[column].values, y_pred[column])
f1_scores.append(score)
return np.mean(f1_scores)
def calc_f1_score(y_true, y_pred):
tp = np.sum(np.multiply([i==True for i in y_pred], y_true))
tn = np.sum(np.multiply([i==False for i in y_pred], [not(j) for j in y_true]))
fp = np.sum(np.multiply([i==True for i in y_pred], [not(j) for j in y_true]))
fn = np.sum(np.multiply([i==False for i in y_pred], y_true))
precision = calc_precision(tp, fp)
recall = calc_recall(tp, fn)
'''if tp != 0 and fp != 0:
precision = calc_precision(tp, fp)
else:
precision = 0
if tp != 0 and fn != 0:
recall = calc_recall(tp, fn)
else:
recall = 0'''
if precision != 0 and recall != 0:
f1 = (2 * precision * recall) / (precision + recall)
else:
f1 = 0
return f1
def calc_precision(tp, fp):
return tp / (tp + fp)
def calc_recall(tp, fn):
return tp / (tp + fn)
def get_score_from_cli():
try:
x = float(input("x: "))
y = float(input("y: "))
abs_v = np.sqrt(x**2 + y**2)
return np.array([abs_v]).reshape(1, -1)
except ValueError:
print("Invalid input. Please enter numeric values.")
return None
def main():
df = load_dataframe()
print(df.head())
sns.countplot(x = df["points"])
plt.show()
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.show()
sns.scatterplot(x=df['x'], y=df['y'], hue=df['points'])
plt.show()
df["radius"] = np.sqrt(df["x"]**2 + df["y"]**2)
X = df[["radius"]]
y = pd.get_dummies(df['points'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
random_forest = RandomForestClassifier(n_estimators=700, random_state=0)
decision_tree = DecisionTreeClassifier(random_state=0)
k_neighbors = KNeighborsClassifier(n_neighbors=5)
models = {
"Random Forest Classifier": random_forest,
"Decision Tree Classifier": decision_tree,
"K-Neighbors": k_neighbors
}
for name, model in models.items():
model.fit(X_train.values, y_train.values)
for name, model in models.items():
pred = model.predict(X_test.values)
my_f1_macro_score = calc_f1_macro(y_test, pd.DataFrame(pred))
print(f'My F1 score of {name} is {my_f1_macro_score}\n')
f1_sklearn = f1_score(y_test.values, pred, average='macro')
print(f'Sklearn F1 score of {name} is {f1_sklearn}\n')
score = get_score_from_cli()
label_encoder = LabelEncoder()
df["points"] = label_encoder.fit_transform(df["points"])
for name, model in models.items():
pred = model.predict(score)
points_number = pd.DataFrame(pred).idxmax(axis=1)
points = label_encoder.inverse_transform(points_number)[0]
print(f"{name}: {points} Punkte")
if __name__ == "__main__":
main()