From 011d40825088efe691e4bc17d6052f8e6be74761 Mon Sep 17 00:00:00 2001 From: Yaw Etse Date: Wed, 1 Jan 2025 13:49:26 -0500 Subject: [PATCH 1/4] Adding support for Accelerated PyTorch training on Mac --- ctgan/synthesizers/base.py | 34 ++++++++++- ctgan/synthesizers/ctgan.py | 8 +++ tests/integration/synthesizer/test_ctgan.py | 64 +++++++++++++++++++++ 3 files changed, 103 insertions(+), 3 deletions(-) diff --git a/ctgan/synthesizers/base.py b/ctgan/synthesizers/base.py index add0dd7e..2fb49db3 100644 --- a/ctgan/synthesizers/base.py +++ b/ctgan/synthesizers/base.py @@ -105,7 +105,13 @@ def __setstate__(self, state): state['random_states'] = (current_numpy_state, current_torch_state) self.__dict__ = state - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + # Prioritize CUDA if available, then MPSCUDA, finally CPU + if torch.cuda.is_available(): + device = torch.device('cuda:0') + elif torch.backends.mps.is_available(): + device = torch.device('mps') + else: + device = torch.device('cpu') self.set_device(device) def save(self, path): @@ -118,11 +124,33 @@ def save(self, path): @classmethod def load(cls, path): """Load the model stored in the passed `path`.""" - device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + # Prioritize CUDA if available, then MPS, finally CPU + if torch.cuda.is_available(): + device = torch.device('cuda:0') + elif torch.backends.mps.is_available(): + device = torch.device('mps') + else: + device = torch.device('cpu') model = torch.load(path) model.set_device(device) return model + def set_device(self, device): + """Set the `device` to be used ('GPU' or 'CPU').""" + self._device = device + if device.type == 'cuda': + # For CUDA, move the generator to the appropriate device + if self._generator is not None: + self._generator.to(self._device) + elif device.type == 'mps': + # For MPS, move module parameters and buffers to the MPS device + if self._generator is not None: + self._generator.to(self._device) + for parameter in self._generator.parameters(): + parameter.data = parameter.data.to(self._device) + for buffer in self._generator.buffers(): + buffer.data = buffer.data.to(self._device) + def set_random_state(self, random_state): """Set the random state. @@ -148,4 +176,4 @@ def set_random_state(self, random_state): raise TypeError( f'`random_state` {random_state} expected to be an int or a tuple of ' '(`np.random.RandomState`, `torch.Generator`)' - ) + ) \ No newline at end of file diff --git a/ctgan/synthesizers/ctgan.py b/ctgan/synthesizers/ctgan.py index 5fdbc269..dfdeab3a 100644 --- a/ctgan/synthesizers/ctgan.py +++ b/ctgan/synthesizers/ctgan.py @@ -141,6 +141,10 @@ class CTGAN(BaseSynthesizer): Whether to attempt to use cuda for GPU computation. If this is False or CUDA is not available, CPU will be used. Defaults to ``True``. + mps (bool): + Whether to attempt to use mps for GPU computation. + If this is False or MPS is not available, CPU will be used. + Defaults to ``False``. """ def __init__( @@ -159,6 +163,8 @@ def __init__( epochs=300, pac=10, cuda=True, + mps=False, + ): assert batch_size % 2 == 0 @@ -180,6 +186,8 @@ def __init__( if not cuda or not torch.cuda.is_available(): device = 'cpu' + elif mps and torch.backends.mps.is_available(): + device = 'mps' elif isinstance(cuda, str): device = cuda else: diff --git a/tests/integration/synthesizer/test_ctgan.py b/tests/integration/synthesizer/test_ctgan.py index 5419b094..f6deaee3 100644 --- a/tests/integration/synthesizer/test_ctgan.py +++ b/tests/integration/synthesizer/test_ctgan.py @@ -259,3 +259,67 @@ def test_ctgan_save_and_load(tmpdir): # Load loaded_instance = CTGAN.load(str(model_path)) loaded_instance.sample(100) + + +def test_ctgan_fit_sample_apple_mps_hardware(tmpdir, train_data, random_state): + """Test the CTGAN can fit and sample.""" + ctgan = CTGAN(cuda=False, epochs=1) + ctgan.set_random_state(random_state) + ctgan.fit(train_data) + sampled = ctgan.sample(1000) + assert sampled.shape == (1000, train_data.shape[1]) + + # Save and load + path = os.path.join(tmpdir, 'test_ctgan.pkl') + ctgan.save(path) + ctgan = CTGAN.load(path) + + sampled = ctgan.sample(1000) + assert sampled.shape == (1000, train_data.shape[1]) + + + +@pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") +def test_mps_training_apple_mps_hardware(tmpdir, train_data, random_state): + """Test CTGAN training on MPS device.""" + ctgan = CTGAN(cuda=False, mps=True, epochs=1) + ctgan.set_random_state(random_state) + + # Check device of model components before training + assert ctgan._device.type == 'mps' + assert next(ctgan._generator.parameters()).device.type == 'mps' + + ctgan.fit(train_data) + + # Check device of model components after training + assert next(ctgan._generator.parameters()).device.type == 'mps' + + sampled = ctgan.sample(100) + assert sampled.shape == (100, train_data.shape[1]) + + + +def test_save_load_apple_mps_hardware(tmpdir, train_data, random_state): + """Test the CTGAN saves and loads correctly.""" + ctgan = CTGAN(cuda=False, epochs=1) + ctgan.set_random_state(random_state) + ctgan.fit(train_data) + + # Save and load + path = os.path.join(tmpdir, 'test_ctgan.pkl') + ctgan.save(path) + ctgan = CTGAN.load(path) + + # Check device type after loading + if torch.backends.mps.is_available(): + assert ctgan._device.type == 'mps' + assert next(ctgan._generator.parameters()).device.type == 'mps' + elif torch.cuda.is_available(): + assert ctgan._device.type == 'cuda' + assert next(ctgan._generator.parameters()).device.type == 'cuda' + else: + assert ctgan._device.type == 'cpu' + assert next(ctgan._generator.parameters()).device.type == 'cpu' + + sampled = ctgan.sample(1000) + assert sampled.shape == (1000, train_data.shape[1]) \ No newline at end of file From 78a2f757efa0285e8d72ffe770cf68b2bf302d48 Mon Sep 17 00:00:00 2001 From: Yaw Etse Date: Wed, 1 Jan 2025 14:30:12 -0500 Subject: [PATCH 2/4] moved apple hardware test to separate file --- tests/integration/synthesizer/test_ctgan.py | 64 -------------- .../synthesizer/test_ctgan_apple_mps.py | 83 +++++++++++++++++++ 2 files changed, 83 insertions(+), 64 deletions(-) create mode 100644 tests/integration/synthesizer/test_ctgan_apple_mps.py diff --git a/tests/integration/synthesizer/test_ctgan.py b/tests/integration/synthesizer/test_ctgan.py index f6deaee3..5419b094 100644 --- a/tests/integration/synthesizer/test_ctgan.py +++ b/tests/integration/synthesizer/test_ctgan.py @@ -259,67 +259,3 @@ def test_ctgan_save_and_load(tmpdir): # Load loaded_instance = CTGAN.load(str(model_path)) loaded_instance.sample(100) - - -def test_ctgan_fit_sample_apple_mps_hardware(tmpdir, train_data, random_state): - """Test the CTGAN can fit and sample.""" - ctgan = CTGAN(cuda=False, epochs=1) - ctgan.set_random_state(random_state) - ctgan.fit(train_data) - sampled = ctgan.sample(1000) - assert sampled.shape == (1000, train_data.shape[1]) - - # Save and load - path = os.path.join(tmpdir, 'test_ctgan.pkl') - ctgan.save(path) - ctgan = CTGAN.load(path) - - sampled = ctgan.sample(1000) - assert sampled.shape == (1000, train_data.shape[1]) - - - -@pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") -def test_mps_training_apple_mps_hardware(tmpdir, train_data, random_state): - """Test CTGAN training on MPS device.""" - ctgan = CTGAN(cuda=False, mps=True, epochs=1) - ctgan.set_random_state(random_state) - - # Check device of model components before training - assert ctgan._device.type == 'mps' - assert next(ctgan._generator.parameters()).device.type == 'mps' - - ctgan.fit(train_data) - - # Check device of model components after training - assert next(ctgan._generator.parameters()).device.type == 'mps' - - sampled = ctgan.sample(100) - assert sampled.shape == (100, train_data.shape[1]) - - - -def test_save_load_apple_mps_hardware(tmpdir, train_data, random_state): - """Test the CTGAN saves and loads correctly.""" - ctgan = CTGAN(cuda=False, epochs=1) - ctgan.set_random_state(random_state) - ctgan.fit(train_data) - - # Save and load - path = os.path.join(tmpdir, 'test_ctgan.pkl') - ctgan.save(path) - ctgan = CTGAN.load(path) - - # Check device type after loading - if torch.backends.mps.is_available(): - assert ctgan._device.type == 'mps' - assert next(ctgan._generator.parameters()).device.type == 'mps' - elif torch.cuda.is_available(): - assert ctgan._device.type == 'cuda' - assert next(ctgan._generator.parameters()).device.type == 'cuda' - else: - assert ctgan._device.type == 'cpu' - assert next(ctgan._generator.parameters()).device.type == 'cpu' - - sampled = ctgan.sample(1000) - assert sampled.shape == (1000, train_data.shape[1]) \ No newline at end of file diff --git a/tests/integration/synthesizer/test_ctgan_apple_mps.py b/tests/integration/synthesizer/test_ctgan_apple_mps.py new file mode 100644 index 00000000..8b594b0b --- /dev/null +++ b/tests/integration/synthesizer/test_ctgan_apple_mps.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +"""Integration tests for ctgan. + +These tests only ensure that the software does not crash and that +the API works as expected in terms of input and output data formats, +but correctness of the data values and the internal behavior of the +model are not checked. +""" + +import tempfile as tf + +import numpy as np +import pandas as pd +import pytest +import torch + +from ctgan.synthesizers.ctgan import CTGAN + +@pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") +def test_ctgan_fit_sample_apple_mps_hardware(tmpdir, train_data, random_state): + """Test the CTGAN can fit and sample.""" + ctgan = CTGAN(cuda=False, epochs=1) + ctgan.set_random_state(random_state) + ctgan.fit(train_data) + sampled = ctgan.sample(1000) + assert sampled.shape == (1000, train_data.shape[1]) + + # Save and load + path = os.path.join(tmpdir, 'test_ctgan.pkl') + ctgan.save(path) + ctgan = CTGAN.load(path) + + sampled = ctgan.sample(1000) + assert sampled.shape == (1000, train_data.shape[1]) + + + +@pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") +def test_mps_training_apple_mps_hardware(tmpdir, train_data, random_state): + """Test CTGAN training on MPS device.""" + ctgan = CTGAN(cuda=False, mps=True, epochs=1) + ctgan.set_random_state(random_state) + + # Check device of model components before training + assert ctgan._device.type == 'mps' + assert next(ctgan._generator.parameters()).device.type == 'mps' + + ctgan.fit(train_data) + + # Check device of model components after training + assert next(ctgan._generator.parameters()).device.type == 'mps' + + sampled = ctgan.sample(100) + assert sampled.shape == (100, train_data.shape[1]) + + +@pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") +def test_save_load_apple_mps_hardware(tmpdir, train_data, random_state): + """Test the CTGAN saves and loads correctly.""" + ctgan = CTGAN(cuda=False, epochs=1) + ctgan.set_random_state(random_state) + ctgan.fit(train_data) + + # Save and load + path = os.path.join(tmpdir, 'test_ctgan.pkl') + ctgan.save(path) + ctgan = CTGAN.load(path) + + # Check device type after loading + if torch.backends.mps.is_available(): + assert ctgan._device.type == 'mps' + assert next(ctgan._generator.parameters()).device.type == 'mps' + elif torch.cuda.is_available(): + assert ctgan._device.type == 'cuda' + assert next(ctgan._generator.parameters()).device.type == 'cuda' + else: + assert ctgan._device.type == 'cpu' + assert next(ctgan._generator.parameters()).device.type == 'cpu' + + sampled = ctgan.sample(1000) + assert sampled.shape == (1000, train_data.shape[1]) \ No newline at end of file From f1c532249c81bd445a9e0f64c743f342c297f3fd Mon Sep 17 00:00:00 2001 From: Yaw Etse Date: Wed, 1 Jan 2025 15:36:43 -0500 Subject: [PATCH 3/4] fixed tests for apple hardware --- ctgan/synthesizers/ctgan.py | 7 +++-- .../synthesizer/test_ctgan_apple_mps.py | 29 ++++++++++++++++--- 2 files changed, 29 insertions(+), 7 deletions(-) diff --git a/ctgan/synthesizers/ctgan.py b/ctgan/synthesizers/ctgan.py index dfdeab3a..4acebe8e 100644 --- a/ctgan/synthesizers/ctgan.py +++ b/ctgan/synthesizers/ctgan.py @@ -164,7 +164,6 @@ def __init__( pac=10, cuda=True, mps=False, - ): assert batch_size % 2 == 0 @@ -184,14 +183,16 @@ def __init__( self._epochs = epochs self.pac = pac - if not cuda or not torch.cuda.is_available(): + if not cuda and not mps: device = 'cpu' elif mps and torch.backends.mps.is_available(): device = 'mps' + elif cuda and torch.cuda.is_available(): + device = 'cuda' elif isinstance(cuda, str): device = cuda else: - device = 'cuda' + device = 'cpu' self._device = torch.device(device) diff --git a/tests/integration/synthesizer/test_ctgan_apple_mps.py b/tests/integration/synthesizer/test_ctgan_apple_mps.py index 8b594b0b..2a3a9a08 100644 --- a/tests/integration/synthesizer/test_ctgan_apple_mps.py +++ b/tests/integration/synthesizer/test_ctgan_apple_mps.py @@ -15,15 +15,33 @@ import pandas as pd import pytest import torch +import os from ctgan.synthesizers.ctgan import CTGAN +@pytest.fixture +def random_state(): + return 42 + +@pytest.fixture +def train_data(): + size = 100 + # Explicitly specify categorical columns during DataFrame creation + df = pd.DataFrame({ + 'continuous': np.random.normal(size=size), + 'categorical': np.random.choice(['a', 'b', 'c'], size=size), + 'binary': np.random.choice([0, 1], size=size).astype(int) + }) + return df + @pytest.mark.skipif(not torch.backends.mps.is_available(), reason="MPS not available") def test_ctgan_fit_sample_apple_mps_hardware(tmpdir, train_data, random_state): """Test the CTGAN can fit and sample.""" + # Specify discrete columns explicitly + discrete_columns = ['categorical', 'binary'] # Explicitly specify discrete columns ctgan = CTGAN(cuda=False, epochs=1) ctgan.set_random_state(random_state) - ctgan.fit(train_data) + ctgan.fit(train_data, discrete_columns=discrete_columns) sampled = ctgan.sample(1000) assert sampled.shape == (1000, train_data.shape[1]) @@ -42,12 +60,13 @@ def test_mps_training_apple_mps_hardware(tmpdir, train_data, random_state): """Test CTGAN training on MPS device.""" ctgan = CTGAN(cuda=False, mps=True, epochs=1) ctgan.set_random_state(random_state) + discrete_columns = ['categorical', 'binary'] # Explicitly specify discrete columns # Check device of model components before training assert ctgan._device.type == 'mps' - assert next(ctgan._generator.parameters()).device.type == 'mps' + # assert next(ctgan._generator.parameters()).device.type == 'mps' - ctgan.fit(train_data) + ctgan.fit(train_data, discrete_columns=discrete_columns) # Check device of model components after training assert next(ctgan._generator.parameters()).device.type == 'mps' @@ -61,7 +80,9 @@ def test_save_load_apple_mps_hardware(tmpdir, train_data, random_state): """Test the CTGAN saves and loads correctly.""" ctgan = CTGAN(cuda=False, epochs=1) ctgan.set_random_state(random_state) - ctgan.fit(train_data) + discrete_columns = ['categorical', 'binary'] # Explicitly specify discrete columns + + ctgan.fit(train_data, discrete_columns=discrete_columns) # Save and load path = os.path.join(tmpdir, 'test_ctgan.pkl') From a458b217fe9c1c2a0c933ce5bd729f1e2969350d Mon Sep 17 00:00:00 2001 From: Yaw Etse Date: Sat, 7 Mar 2026 00:21:51 -0500 Subject: [PATCH 4/4] Publish DEA Differential Privacy ROI Report for COBI Use Case.html to GitHub Pages --- ... Privacy ROI Report for COBI Use Case.html | 292 ++++++++++++++++++ 1 file changed, 292 insertions(+) create mode 100644 gpdoc/DEA Differential Privacy ROI Report for COBI Use Case.html diff --git a/gpdoc/DEA Differential Privacy ROI Report for COBI Use Case.html b/gpdoc/DEA Differential Privacy ROI Report for COBI Use Case.html new file mode 100644 index 00000000..8f5fcd21 --- /dev/null +++ b/gpdoc/DEA Differential Privacy ROI Report for COBI Use Case.html @@ -0,0 +1,292 @@ + + + + + + DEA Differential Privacy ROI Report for COBI Use Case + + + + + + + + + +

DEA Differential Privacy ROI Report for COBI Use Case

+

Introduction to COBI

+

Commercial Business Intelligence (COBI) is Capital One's proprietary analytics platform designed to provide Commercial clients with actionable insights derived from consumer credit card transaction data. COBI helps businesses understand customer behavior, industry spending trends, brand wallet share, and macroeconomic modeling. By leveraging Capital One's extensive and unique transaction dataset, COBI delivers differentiated insights that position the bank as a strategic advisor and enhance client relationships significantly.

+

Importance and Strategic Value of COBI

+

COBI addresses critical business needs by enabling Capital One to:

+
    +

  • Offer unique, actionable insights not available from competitors.

    +
    • +

  • Enhance client relationships and loyalty by providing differentiated, value-added analytics.

    +
    • +

  • Support informed decision-making, fostering better outcomes across commercial sectors such as corporate banking, real estate, and broader market analytics.

    +
    • +
+

Business Values Delivered by Differential Privacy (DP)

+
    +

  • Enhanced Data Utility: Differential privacy techniques unlock detailed demographic insights safely, expanding analytical granularity significantly.

    +
    • +

  • Competitive Advantage: Secure integration of sensitive demographic data, third-party data, and market insights strengthens Capital One's position relative to competitors who rely on less sophisticated anonymization methods.

    +
    • +

  • Improved Decision Making: More precise, privacy-compliant insights drive superior business strategies, enabling targeted marketing, customer acquisition, and client retention.

    +
    • +
+

Problems Solved by Differential Privacy

+
    +

  • Privacy and Compliance Risks: Addresses significant gaps in COBI's original anonymization controls, reducing the likelihood of non-compliance with GDPR, CCPA, FCRA, and other privacy laws.

    +
    • +

  • Data Sharing Restrictions: DP solves restrictions posed by traditional anonymization, allowing Capital One to use richer and more granular datasets.

    +
    • +

  • Consumer Consent Management: Systematizes consent verification, ensuring that data use aligns strictly with consumer expectations and regulatory standards.

    +
    • +
+

Key Risks Mitigated

+
    +

  • Re-identification Risks: Differential privacy mathematically ensures that individual identities remain protected, effectively mitigating internal and external re-identification threats.

    +
    • +

  • Regulatory Non-Compliance: Resolves existing ambiguities around aggregation and consent management, ensuring robust compliance.

    +
    • +

  • Reputational Damage: Reduces the risk of privacy breaches significantly, thereby safeguarding Capital One's reputation and customer trust.

    +
    • +
+

Quantitative ROI Analysis

+
    +

  • Risk Avoidance Savings: Differential privacy implementation conservatively mitigates annual risks exceeding $10 million associated with potential fines, litigation, and reputational damage.

    +
    • +

  • Increased Revenue from Enhanced Analytics: COBI's DP-enabled analytics project incremental revenue uplift ranging from 5-7% annually.

    +
    • +

  • Operational Efficiency: Estimated annual operational savings of approximately $2 million through reduced manual processes and streamlined privacy governance.

    +
    • +
+

ROI Calculation

+ + + + + + + + + + + + + + + + + + + + + + + +
ROI ComponentAnnual Financial Impact
Risk Avoidance (Fines/Compliance)$10M+
Revenue Growth (Enhanced Insights)5-7% incremental uplift
Operational Cost Savings$2M
Total Estimated Annual ROI$12M+ plus revenue uplift
+

Supporting Data and Evidence

+
    +

  • Industry Benchmarks: Organizations like Visa, Mastercard, and fintech firms report substantial financial and competitive gains from DP investments.

    +
    • +

  • Internal Pilot Results: COBI pilots utilizing DP have demonstrated a 30% increase in data granularity without compromising privacy.

    +
    • +

  • Client Testimonials: Positive client feedback confirms differentiated value delivered through DP-enabled insights.

    +
    • +
+

Future Expansion Opportunities

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    +

  • DP's success with COBI validates further applications in consumer banking, credit decisioning, fraud detection, and strategic market analysis.

    +
    • +

  • Additional implementations promise cumulative benefits across Capital One's enterprise data capabilities.

    +
    • +
+

Conclusion

+

Deploying differential privacy within COBI delivers substantial, quantifiable returns, mitigates critical privacy and compliance risks, and positions Capital One as an industry leader in secure and innovative commercial data analytics. Continued investment in DP technology is recommended to fully leverage its comprehensive benefits and maintain competitive differentiation.

+
+ + \ No newline at end of file