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1a0e6e8 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import numpy as np
import os
def interpret_correlation(rho):
abs_rho = abs(rho)
if abs_rho < 0.20:
return 'sangat lemah'
elif abs_rho < 0.40:
return 'lemah'
elif abs_rho < 0.60:
return 'sedang'
elif abs_rho < 0.80:
return 'kuat'
else:
return 'sangat kuat'
def run_correlation_analysis(csv_path: str):
df = pd.read_csv(csv_path)
results = []
for layer in range(1, 13):
col_name = f'Score L{layer}'
# Dropna just in case
valid_data = df.dropna(subset=[col_name, 'rating'])
spearman_rho, spearman_p = stats.spearmanr(valid_data[col_name], valid_data['rating'])
pearson_r, pearson_p = stats.pearsonr(valid_data[col_name], valid_data['rating'])
interpretation = interpret_correlation(spearman_rho)
results.append({
'layer': col_name,
'spearman_rho': spearman_rho,
'spearman_p': spearman_p,
'pearson_r': pearson_r,
'pearson_p': pearson_p,
'interpretasi_spearman': interpretation
})
df_results = pd.DataFrame(results)
return df, df_results
def plot_correlation_bar(df_corr):
fig, ax = plt.subplots(figsize=(10, 6))
ax.bar(df_corr['layer'], df_corr['spearman_rho'])
ax.set_title('Korelasi Spearman (rho) per Layer vs Rating Ustadz')
ax.set_xlabel('Layer')
ax.set_ylabel('Spearman rho')
plt.xticks(rotation=45)
fig.tight_layout()
return fig
def plot_scatter_best_layer(df, best_layer):
fig, ax = plt.subplots(figsize=(8, 6))
valid_data = df.dropna(subset=[best_layer, 'rating'])
x = valid_data[best_layer]
y = valid_data['rating']
ax.scatter(x, y, alpha=0.5, label='Data points')
# Linear regression line
m, b = np.polyfit(x, y, 1)
ax.plot(x, m*x + b, label=f'Trend line')
ax.set_title(f'Scatter Plot: {best_layer} vs Rating Ustadz')
ax.set_xlabel(f'Skor Sistem ({best_layer})')
ax.set_ylabel('Rating Ustadz')
ax.legend()
fig.tight_layout()
return fig
def plot_heatmap(df_corr):
fig, ax = plt.subplots(figsize=(10, 4))
# Create a simple heatmap
data = df_corr[['spearman_rho', 'pearson_r']].values.T
cax = ax.imshow(data, aspect='auto')
# Add values
for i in range(data.shape[0]):
for j in range(data.shape[1]):
ax.text(j, i, f'{data[i, j]:.2f}', ha='center', va='center', color='black')
ax.set_yticks([0, 1])
ax.set_yticklabels(['Spearman rho', 'Pearson r'])
ax.set_xticks(range(len(df_corr)))
ax.set_xticklabels(df_corr['layer'], rotation=45)
ax.set_title('Heatmap Korelasi')
fig.colorbar(cax)
fig.tight_layout()
return fig
def plot_pairing_diagram(df):
# Get unique participants and files
participants = df['ID_Peserta'].unique()[:1]
files = df['ID_Frasa'].unique()
# Sesuaikan ukuran agar tidak terlalu bertumpuk jika datanya banyak
height = max(5, max(len(participants), len(files)) * 0.4)
fig, ax = plt.subplots(figsize=(12, height))
# Positions
x_peserta = 1
x_frasa = 2
x_ref = 3
# Draw nodes
y_peserta = np.linspace(len(files), 1, len(files))
y_frasa = np.linspace(len(files), 1, len(files))
y_ref = np.linspace(len(files), 1, len(files))
# Peserta nodes
ax.scatter([x_peserta]*len(files), y_peserta, s=200, zorder=2)
if len(participants) > 0:
p_name = participants[0]
for i, f in enumerate(files):
ax.annotate(f"Peserta {p_name} (Rekaman {i+1})", (x_peserta - 0.1, y_peserta[i]), ha='right', va='center', fontsize=10)
# Frasa nodes
ax.scatter([x_frasa]*len(files), y_frasa, s=200, zorder=2)
for i, f in enumerate(files):
# f is filename, format slightly for display e.g. "01.wav" -> "Frasa 1"
frasa_label = f"Frasa {i+1}"
ax.annotate(frasa_label, (x_frasa, y_frasa[i] + 0.15), ha='center', va='bottom', fontsize=10)
# Referensi nodes
ax.scatter([x_ref]*len(files), y_ref, s=200, zorder=2)
for i, f in enumerate(files):
ax.annotate(f"Referensi {i+1}", (x_ref + 0.1, y_ref[i]), ha='left', va='center', fontsize=10)
# Draw lines
for j, _ in enumerate(files):
# Peserta to Frasa
ax.plot([x_peserta, x_frasa], [y_peserta[j], y_frasa[j]], zorder=1, alpha=0.5)
for j, _ in enumerate(files):
# Frasa to Referensi
ax.plot([x_frasa, x_ref], [y_frasa[j], y_ref[j]], zorder=1, alpha=0.5)
peserta_name = participants[0] if len(participants) > 0 else "Peserta"
ax.set_title(f"Ilustrasi Struktur Dataset Pasangan Frasa", fontsize=14)
ax.set_xlim(0.5, 3.5)
ax.set_ylim(0, len(files) + 1)
ax.axis('off')
fig.tight_layout()
return fig
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