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FP-test-set notebook
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‎notebooks/unsupervised bias detection tool/COMPAS_FP.ipynb‎

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},
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{
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"cell_type": "code",
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"metadata": {},
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{
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"4 0.0 1.0 5.0 0.0 0.0 0.0 0.0"
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"cell_type": "code",
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"dtype: object"
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"metadata": {},
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},
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"cell_type": "code",
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"metadata": {},
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],
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"source": [
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"# Create a dataframe from filtered_df except the bias metric column\n",
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"df_no_bias_metric = filtered_df.drop(columns=[\"false_positive\"])\n",
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"bias_metric = \"false_positive\"\n",
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"df_no_bias_metric = filtered_df.drop(columns=[bias_metric])\n",
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"if df_no_bias_metric.dtypes.nunique() == 1:\n",
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" print('consistent data')\n",
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"else:\n",
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},
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"cell_type": "code",
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"cell_type": "code",
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},
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"cell_type": "code",
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"metadata": {},
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],
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"source": [
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"# split the data into training and testing sets\n",
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"bias_metric = \"false_positive\"\n",
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"train_df, test_df = train_test_split(filtered_df, test_size=0.2, random_state=42)\n",
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"X_train = train_df.drop(columns=[bias_metric])\n",
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"\n",
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},
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"BiasAwareHierarchicalKModes(bahc_max_iter=20, bahc_min_cluster_size=57.71)"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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"outputs": [
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{
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"5"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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},
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"metadata": {},
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"outputs": [
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{
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"array([ 0.05290941, -0.02457072, -0.04827102, -0.04888889, -0.05093596])"
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]
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},
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"metadata": {},
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},
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"cell_type": "code",
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},
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{
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"cell_type": "code",
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{
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"array([0, 0, 4, ..., 0, 0, 1], dtype=uint32)"
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]
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},
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{
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"[1443 rows x 7 columns]"
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]
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},
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"output_type": "execute_result"
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{
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"4 0 0 0 "
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"metadata": {
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"metadata": {
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"vscode": {
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"languageId": "ruby"

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