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System save at 22/09/2025 17:22 by user_client2024

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user_client2024 2025-09-22 11:52:02 +00:00
parent bdac4bc553
commit ba80816eca
3 changed files with 6 additions and 6 deletions
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4
.ipynb_checkpoints/main-checkpoint.ipynb
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@ -29,8 +29,8 @@
" \"\"\"\n",
"\n",
" # Calculate thresholds\n",
" th1 = np.percentile(df[var1].dropna(), 50)\n",
" th2 = np.percentile(df[var2].dropna(), 50)\n",
" th1 = np.percentile(df[var1].dropna(), 80)\n",
" th2 = np.percentile(df[var2].dropna(), 80)\n",
" th3 = np.percentile(df[var3].dropna(), 90)\n",
"\n",
" # Split into alerting and non-alerting\n",

4
main.ipynb
View File

@ -29,8 +29,8 @@
" \"\"\"\n",
"\n",
" # Calculate thresholds\n",
" th1 = np.percentile(df[var1].dropna(), 50)\n",
" th2 = np.percentile(df[var2].dropna(), 50)\n",
" th1 = np.percentile(df[var1].dropna(), 80)\n",
" th2 = np.percentile(df[var2].dropna(), 80)\n",
" th3 = np.percentile(df[var3].dropna(), 90)\n",
"\n",
" # Split into alerting and non-alerting\n",

4
main.py
View File

@ -26,8 +26,8 @@ def apply_sar_flag(df, var1, var2, var3, random_state=42):
"""
# Calculate thresholds
th1 = np.percentile(df[var1].dropna(), 50)
th2 = np.percentile(df[var2].dropna(), 50)
th1 = np.percentile(df[var1].dropna(), 80)
th2 = np.percentile(df[var2].dropna(), 80)
th3 = np.percentile(df[var3].dropna(), 90)
# Split into alerting and non-alerting