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from module.constants import (
HOUSE_COLORS,
NUMERICAL_FEATURE_CSV_TITLES,
HOUSE_FEATURE_CSV_TITLE,
)
from module.dataset_manip import parse_csv
import matplotlib.pyplot as plt
import os
import pandas as pd
import sys
if len(sys.argv) < 2:
print(f"Usage: python {__file__} <dataset.csv>")
exit(-1)
# Get data from CSV file
dataset_filename = sys.argv[1]
data = parse_csv(
dataset_filename, NUMERICAL_FEATURE_CSV_TITLES, [HOUSE_FEATURE_CSV_TITLE]
)
df = pd.DataFrame(data)
# Show a scatter plot for each combination of numerical feature
numerical_feature_count = len(NUMERICAL_FEATURE_CSV_TITLES)
for i in range(numerical_feature_count):
for j in range(i + 1, numerical_feature_count):
x_axis_feature = NUMERICAL_FEATURE_CSV_TITLES[i]
y_axis_feature = NUMERICAL_FEATURE_CSV_TITLES[j]
title = f"{x_axis_feature} vs {y_axis_feature} Scatter Plot"
fig, ax = plt.subplots()
for house in df[HOUSE_FEATURE_CSV_TITLE].dropna().unique():
house_df = df.loc[df[HOUSE_FEATURE_CSV_TITLE] == house]
ax.scatter(
house_df.loc[:, x_axis_feature],
house_df.loc[:, y_axis_feature],
alpha=0.8,
color=HOUSE_COLORS[house.lower()],
label=house,
s=10,
)
ax.set_title(title)
ax.set_xlabel(f"{x_axis_feature} Score")
ax.set_ylabel(f"{y_axis_feature} Score")
ax.legend()
# Save to png file
os.makedirs("output/scatter_plot", exist_ok=True)
save_filename = f"output/scatter_plot/{title}.png"
fig.savefig(save_filename)
plt.close(fig)
print(f"Saved {title} to {save_filename}")
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