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Added executed notebook
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@JayanaGunaweera01
JayanaGunaweera01 committed Apr 1, 2024
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Showing 1 changed file with 33 additions and 97 deletions.
130 changes: 33 additions & 97 deletions automation/output_demo_notebook.ipynb
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{
"cell_type": "code",
"execution_count": 1,
"id": "0f294200",
"id": "d3c389e5",
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-01T09:08:15.715634Z",
"iopub.status.busy": "2024-04-01T09:08:15.715197Z",
"iopub.status.idle": "2024-04-01T09:10:04.815215Z",
"shell.execute_reply": "2024-04-01T09:10:04.814527Z"
"iopub.execute_input": "2024-04-01T10:22:02.057930Z",
"iopub.status.busy": "2024-04-01T10:22:02.057496Z",
"iopub.status.idle": "2024-04-01T10:23:48.054641Z",
"shell.execute_reply": "2024-04-01T10:23:48.053980Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-04-01 09:08:36.236889: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-04-01 09:08:37.120837: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /opt/hostedtoolcache/Python/3.11.8/x64/lib/python3.11/site-packages/tensorflow/python/compat/v2_compat.py:98: disable_resource_variables (from tensorflow.python.ops.resource_variables_toggle) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"non-resource variables are not supported in the long term\n"
]
},
{
"name": "stderr",
"output_type": "stream",
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"pip install 'aif360[LawSchoolGPA]'\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-04-01 10:22:43.682702: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-04-01 10:22:44.657335: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
},
{
"name": "stderr",
"output_type": "stream",
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"import time\n",
"\n",
"# Add a sleep statement to wait for imports to be fully loaded\n",
"time.sleep(20) \n",
"\n",
"# Create a TensorFlow session\n",
"import tensorflow.compat.v1 as tf\n",
"tf.disable_v2_behavior()\n",
"sess = tf.Session()\n",
"time.sleep(20) # You can adjust the sleep duration based on your needs\n",
"\n",
"from aif360.datasets import GermanDataset\n",
"time.sleep(20)\n",
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"from IPython.display import Markdown, display\n",
"time.sleep(20)\n",
"\n",
"# Function to initialize the Reweighing algorithm\n",
"def initialize_reweighing(unprivileged_groups, privileged_groups):\n",
" return Reweighing(unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"\n",
"# Function to initialize the AdversarialDebiasing algorithm\n",
"def initialize_adversarial_debiasing(unprivileged_groups, privileged_groups, adversary_loss_weight, num_epochs, batch_size, classifier_num_hidden_units, debias, sess):\n",
" scope_name = \"my_scope\"\n",
" return AdversarialDebiasing(unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups,\n",
" scope_name=scope_name,\n",
" sess=sess,\n",
" seed=None,\n",
" adversary_loss_weight=adversary_loss_weight,\n",
" num_epochs=num_epochs,\n",
" batch_size=batch_size,\n",
" classifier_num_hidden_units=classifier_num_hidden_units,\n",
" debias=debias)\n",
"\n",
"# Function to initialize the OptimPreproc algorithm\n",
"def initialize_optim_preproc(unprivileged_groups, privileged_groups, optimizer, optim_options, verbose):\n",
" return OptimPreproc(optimizer=optimizer,\n",
" optim_options=optim_options,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups,\n",
" verbose=verbose,\n",
" seed=None)\n",
"\n",
"# Load dataset\n",
"dataset_orig = GermanDataset(\n",
" protected_attribute_names=['age'],\n",
" privileged_classes=[lambda x: x >= 25],\n",
" features_to_drop=['personal_status', 'sex']\n",
" protected_attribute_names=['age'], # this dataset also contains protected\n",
" # attribute for \"sex\" which we do not\n",
" # consider in this evaluation\n",
" privileged_classes=[lambda x: x >= 25], # age >=25 is considered privileged\n",
" features_to_drop=['personal_status', 'sex'] # ignore sex-related attributes\n",
")\n",
"\n",
"# Split dataset\n",
"dataset_orig_train, dataset_orig_test = dataset_orig.split([0.7], shuffle=True)\n",
"\n",
"# Define privileged and unprivileged groups\n",
"privileged_groups = [{'age': 1}]\n",
"unprivileged_groups = [{'age': 0}]\n",
"\n",
"# Define metric\n",
"metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train, \n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"display(Markdown(\"#### Original training dataset\"))\n",
"print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_train.mean_difference())\n",
"\n",
"# Initialize ALG as None initially\n",
"ALG = None\n",
"\n",
"# User-inputted algorithm\n",
"algorithm_choice = \"Reweighing\"\n",
"\n",
"# Initialize algorithm based on user input \n",
"if algorithm_choice == \"Reweighing\":\n",
" ALG = initialize_reweighing(unprivileged_groups, privileged_groups)\n",
"elif algorithm_choice == \"AdversarialDebiasing\":\n",
" ALG = initialize_adversarial_debiasing(unprivileged_groups, privileged_groups, \n",
" adversary_loss_weight=0.1, num_epochs=50, batch_size=128, \n",
" classifier_num_hidden_units=200, debias=True, sess=sess)\n",
"elif algorithm_choice == \"OptimPreproc\":\n",
" ALG = initialize_optim_preproc(unprivileged_groups, privileged_groups, \n",
" optimizer='my_optimizer', optim_options={'option1': 'value1', 'option2': 'value2'}, \n",
" verbose=False)\n",
"RW = Reweighing(unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"dataset_transf_train = RW.fit_transform(dataset_orig_train)\n",
"\n",
"# Fit and transform dataset\n",
"dataset_transf_train = ALG.fit_transform(dataset_orig_train)\n",
"\n",
"# Compute metric on transformed dataset\n",
"metric_transf_train = BinaryLabelDatasetMetric(dataset_transf_train, \n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"\n",
"# Display results\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"display(Markdown(\"#### Transformed training dataset\"))\n",
"print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_transf_train.mean_difference())\n"
]
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