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added argv support with class Arguments and Enum

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This commit is contained in:
Sandro Zimmermann 2025-11-29 16:16:16 +01:00
parent 3bcbbdf544
commit c1a72b706d
9 changed files with 25098 additions and 2001 deletions
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BIN
__pycache__/arguments.cpython-313.pyc Normal file
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13
absolut.py
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@ -15,7 +15,7 @@ FEATURES = ["points", "x", "y"]
def load_dataframe():
try:
colum_list = FEATURES
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.abs()
except FileNotFoundError as error:
print(error)
@ -34,13 +34,10 @@ def calc_f1_score(y_true, y_pred):
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)'''
recall = calc_recall(tp, fn)
if tp != 0 and fp != 0:
'''if tp != 0 and fp != 0:
precision = calc_precision(tp, fp)
else:
precision = 0
@ -48,7 +45,7 @@ def calc_f1_score(y_true, y_pred):
if tp != 0 and fn != 0:
recall = calc_recall(tp, fn)
else:
recall = 0
recall = 0'''
if precision != 0 and recall != 0:
f1 = (2 * precision * recall) / (precision + recall)
@ -77,7 +74,6 @@ def main():
print(df.head())
print(df.head().info())
sns.countplot(x = df["points"])
plt.show()
@ -86,7 +82,6 @@ def main():
sns.scatterplot(x=df['x'], y=df['y'], hue=df['points'])
plt.show()
quit()
features = ["x", "y"]
X = df[features]

52
arguments.py Normal file
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@ -0,0 +1,52 @@
from enum import Enum
class Mode(Enum):
V = "v"
A = "a"
C = "c"
class Information(Enum):
DISABLED = False
ENABLED = True
class Graph(Enum):
DISABLED = False
ENABLED = True
class Arguments:
def __init__(self, file_path):
self.file_path = file_path
self.mode = None
self.information = None
self.graph = None
def get_file_path(self):
return self.file_path
def get_mode(self):
return self.mode
def set_mode(self, value):
try:
self.mode = Mode(value)
except ValueError:
raise ValueError(f"Invalid mode '{value}'. Allowed values: {[m.value for m in Mode]}")
def get_information(self):
return self.information
def set_information(self, value):
try:
self.information = Information(value)
except ValueError:
raise ValueError(f"Invalid information '{value}'. Allowed values: {[m.value for m in Information]}")
def get_graph(self):
return self.graph
def set_graph(self, value):
try:
self.graph = Graph(value)
except ValueError:
raise ValueError(f"Invalid graph '{value}'. Allowed values: {[m.value for m in Graph]}")

955
data/shots_dev.csv
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@ -1,955 +0,0 @@
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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
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13 6 5.0 0.0
14 0 -10.5 -4.0
15 7 -3.5 1.5
16 0 -10.5 -1.0
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18 8 -1.0 2.5
19 8 2.5 -1.5
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21 6 -4.0 -3.0
22 6 -4.5 -1.0
23 7 4.0 -0.5
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25 4 6.5 4.0
26 8 2.5 -1.5
27 7 -2.5 -2.5
28 7 -1.0 3.5
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32 9 2.5 -1.5
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36 6 5.0 2.0
37 2 8.5 -3.5
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40 4 -7.5 1.0
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43 8 1.0 -3.0
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51 6 4.5 -1.5
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53 1 10.0 2.5
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154 4 7.0 1.0
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236 8 -2.5 -1.5
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255 7 -3.5 -1.0
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325 9 1.5 1.5
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394 6 5.0 0.0
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BIN
data/shots_dev.xlsx
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1001
data/synthetic_data.csv
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25001
data/synthetic_shots.csv Normal file
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54
rule_based_values.py → generate_synthetic_shots.py
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@ -1,23 +1,26 @@
# 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)
'''--------------------------------------------------
Script randomly generates coordinates from (-10, -10)
to (10, 10), with step 0.5.
Then applies points from 0 to 10 to the coordinates.
Points value is determined by circle area.
Export the dataset to "data/synthetic_shots.csv"
---------------------------------------------------'''
import sys
import pandas as pd
import numpy as np
import csv
FEATURES = ["points", "x", "y"]
args = sys.argv[1:]
if not args:
print("Usage: python3 generate_synthetic_shots.py <number of generated shots>")
sys.exit(1)
n = int(args[0])
# Area circle
A10 = 1.5 ** 2 * np.pi
@ -31,21 +34,9 @@ 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) # fromn -10 to 10 with step 0.5
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)]
xy = [(np.random.choice(possible_values), np.random.choice(possible_values)) for _ in range(n)]
dataset = []
@ -77,9 +68,8 @@ for i in xy:
elif A > A1:
dataset.append([0, i[0], i[1]])
#print(dataset)
with open('data/synthetic_data.csv', 'w', newline='') as csvfile:
with open('data/synthetic_shots.csv', 'w', newline='') as csvfile:
fieldnames = ['points', 'x', 'y']
writer = csv.writer(csvfile)
writer.writerow(fieldnames)
writer.writerows(dataset)

23
main.py
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@ -1,3 +1,5 @@
import sys
from arguments import Arguments
import pandas as pd
import numpy as np
import seaborn as sns
@ -9,14 +11,27 @@ from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder
if not sys.argv[1:]:
print("Usage: python3 main.py <path to csv> <mode vector [v] (default) or absolut [a] or cartesian [c]> <optional information [true]> <optional graphs [true]>")
sys.exit(1)
args = Arguments(sys.argv[1])
args.set_mode(sys.argv[2])
try:
args.set_information(sys.argv[3])
args.set_graph(sys.argv[4])
except IndexError:
args.set_information(False)
args.set_graph(False)
FEATURES = ["points", "x", "y"]
# create dataframe from csv and drop any row with null values
def load_dataframe():
def load_dataframe(file_path):
try:
colum_list = FEATURES
#df = pd.read_csv("data/shots.csv", usecols = colum_list).dropna()
df = pd.read_csv("data/synthetic_data.csv", usecols = colum_list).dropna()
df = pd.read_csv(file_path, usecols = colum_list).dropna()
return df
except FileNotFoundError as error:
print(error)
@ -73,7 +88,7 @@ def get_score_from_cli():
return None
def main():
df = load_dataframe()
df = load_dataframe(args.get_file_path())
print(df.describe())
#print(df.head())
#print(df.head().info())