PATEGAN base implementation

Remove duplicate test files after renaming

Use BaseModel variables

requirements.txt

@@ -9,3 +9,4 @@ pmlb==1.0.*
 tqdm<5.0
 typeguard==2.13.*
 pytest==6.2.*
+tensorflow_probability==0.12.*

src/ydata_synthetic/synthesizers/regular/__init__.py

@@ -5,6 +5,7 @@
 from ydata_synthetic.synthesizers.regular.dragan.model import DRAGAN
 from ydata_synthetic.synthesizers.regular.cramergan.model import CRAMERGAN
 from ydata_synthetic.synthesizers.regular.cwgangp.model import CWGANGP
+from ydata_synthetic.synthesizers.regular.pategan.model import PATEGAN
 
 __all__ = [
     "VanilllaGAN",
@@ -14,4 +15,5 @@
     "DRAGAN",
     "CRAMERGAN",
     "CWGANGP"
+    "PATEGAN"
 ]

src/ydata_synthetic/synthesizers/regular/pategan/model.py

@@ -0,0 +1,256 @@
+"PATEGAN implementation supporting Differential Privacy budget specification."
+# pylint: disable = W0622, E0401
+from math import log
+from typing import List, NamedTuple, Optional
+
+import tqdm
+from tensorflow import (GradientTape, clip_by_value, concat, constant,
+                        expand_dims, ones_like, tensor_scatter_nd_update,
+                        transpose, zeros, zeros_like)
+from tensorflow.data import Dataset
+from tensorflow.dtypes import cast, float64, int64
+from tensorflow.keras import Model
+from tensorflow.keras.layers import Dense, Input, ReLU
+from tensorflow.keras.losses import BinaryCrossentropy
+from tensorflow.keras.optimizers import Adam
+from tensorflow.math import abs, exp, pow, reduce_sum, square
+from tensorflow.random import uniform
+from tensorflow_probability import distributions
+
+from ydata_synthetic.synthesizers import TrainParameters
+from ydata_synthetic.synthesizers.gan import BaseModel
+from ydata_synthetic.utils.gumbel_softmax import ActivationInterface
+
+
+# pylint: disable=R0902
+class PATEGAN(BaseModel):
+    "A basic PATEGAN synthesizer implementation with configurable differential privacy budget."
+
+    __MODEL__='PATEGAN'
+
+    def __init__(self, model_parameters, n_teachers: int, target_delta: float, target_epsilon: float):
+        super().__init__(model_parameters)
+        self.n_teachers = n_teachers
+        self.target_epsilon = target_epsilon
+        self.target_delta = target_delta
+
+    # pylint: disable=W0201
+    def define_gan(self, processor_info: Optional[NamedTuple] = None):
+        def discriminator():
+            return Discriminator(self.batch_size).build_model((self.data_dim,), self.layers_dim)
+
+        self.generator = Generator(self.batch_size). \
+            build_model(input_shape=(self.noise_dim,), dim=self.layers_dim, data_dim=self.data_dim,
+                        processor_info=processor_info)
+        self.s_discriminator = discriminator()
+        self.t_discriminators = [discriminator() for i in range(self.n_teachers)]
+
+        generator_optimizer = Adam(learning_rate=self.g_lr)
+        discriminator_optimizer = Adam(learning_rate=self.d_lr)
+
+        loss_fn = BinaryCrossentropy(from_logits=True)
+        self.generator.compile(loss=loss_fn, optimizer=generator_optimizer)
+        self.s_discriminator.compile(loss=loss_fn, optimizer=discriminator_optimizer)
+        for teacher in self.t_discriminators:
+            teacher.compile(loss=loss_fn, optimizer=discriminator_optimizer)
+
+    # pylint: disable = C0103
+    @staticmethod
+    def _moments_acc(n_teachers, votes, lap_scale, l_list):
+        q = (2 + lap_scale * abs(2 * votes - n_teachers))/(4 * exp(lap_scale * abs(2 * votes - n_teachers)))
+
+        update = []
+        for l in l_list:
+            clip = 2 * square(lap_scale) * l * (l + 1)
+            t = (1 - q) * pow((1 - q) / (1 - exp(2 * lap_scale) * q), l) + q * exp(2 * lap_scale * l)
+            update.append(reduce_sum(clip_by_value(t, clip_value_min=-clip, clip_value_max=clip)))
+        return cast(update, dtype=float64)
+
+    def get_data_loader(self, data) -> List[Dataset]:
+        "Obtain a List of TF Datasets corresponding to partitions for each teacher in n_teachers."
+        loader = []
+        SHUFFLE_BUFFER_SIZE = 100
+
+        for teacher_id in range(self.n_teachers):
+            start_id = int(teacher_id * len(data) / self.n_teachers)
+            end_id = int((teacher_id + 1) * len(data) / self.n_teachers if \
+                teacher_id != (self.n_teachers - 1) else len(data))
+            loader.append(Dataset.from_tensor_slices(data[start_id:end_id:])\
+                .batch(self.batch_size).shuffle(SHUFFLE_BUFFER_SIZE))
+        return loader
+
+    # pylint:disable=R0913
+    def train(self, data, class_ratios, train_arguments: TrainParameters, num_cols: List[str], cat_cols: List[str]):
+        """
+        Args:
+            data: A pandas DataFrame or a Numpy array with the data to be synthesized
+            class_ratios:
+            train_arguments: GAN training arguments.
+            num_cols: List of columns of the data object to be handled as numerical
+            cat_cols: List of columns of the data object to be handled as categorical
+        """
+        super().train(data, num_cols, cat_cols)
+
+        data = self.processor.transform(data)
+        self.data_dim = data.shape[1]
+        self.define_gan(self.processor.col_transform_info)
+
+        self.class_ratios = class_ratios
+
+        alpha = cast([0.0 for _ in range(train_arguments.num_moments)], float64)
+        l_list = 1 + cast(range(train_arguments.num_moments), float64)
+
+        # print("initial alpha", l_list.shape)
+
+        cross_entropy = BinaryCrossentropy(from_logits=True)
+
+        generator_optimizer = Adam(learning_rate=train_arguments.lr)
+        disc_opt_stu = Adam(learning_rate=train_arguments.lr)
+        disc_opt_t = [Adam(learning_rate=train_arguments.lr) for i in range(self.n_teachers)]
+
+        train_loader = self.get_data_loader(data, self.batch_size)
+
+        steps = 0
+        epsilon = 0
+
+        category_samples = distributions.Categorical(probs=self.class_ratios, dtype=float64)
+
+        while epsilon < self.target_epsilon:
+            # train the teacher descriminator
+            for t_2 in range(train_arguments.num_teacher_iters):
+                for i in range(self.n_teachers):
+                    inputs, categories = None, None
+                    for b, data_ in enumerate(train_loader[i]):
+                        inputs, categories = data_, b  # categories = 0, data_ holds the first batch, why do we do this?
+                        #categories will give zero value in each loop as the loop break after running the first time
+                        #inputs will have only the first batch of data
+                        break
+
+                    with GradientTape() as disc_tape:
+                        # train with real
+                        dis_data = concat([inputs, zeros((self.batch_size, 1), dtype=float64)], 1)  # Why do we append a column of zeros instead of categories?
+                        # print("1st batch data", dis_data.shape)
+                        real_output = self.t_discriminators[i](dis_data, training=True)
+                        # print(real_output.shape, tf.ones.shape)
+
+                        # train with fake
+                        z = uniform([self.batch_size, self.noise_dim], dtype=float64)
+                        # print("uniformly distributed noise", z.shape)
+
+                        sample = expand_dims(category_samples.sample(self.batch_size), axis=1)
+                        # print("category", sample.shape)
+
+                        fake = self.generator(concat([z, sample], 1))
+                        # print('fake', fake.shape)
+
+                        fake_output = self.t_discriminators[i](concat([fake, sample], 1), training=True)
+                        # print('fake_output_dis', fake_output.shape)
+
+                        # print("watch", disc_tape.watch(self.teacher_disc[i].trainable_variables)
+                        real_loss_disc = cross_entropy(ones_like(real_output), real_output)
+                        fake_loss_disc = cross_entropy(zeros_like(fake_output), fake_output)
+
+                        disc_loss = real_loss_disc + fake_loss_disc
+                        # print(disc_loss, real_loss_disc, fake_loss_disc)
+
+                        disc_grad = disc_tape.gradient(disc_loss, self.t_discriminators[i].trainable_variables)
+                        # print(gradients_of_discriminator)
+
+                        disc_opt_t[i].apply_gradients(zip(disc_grad, self.t_discriminators[i].trainable_variables))
+
+            # train the student discriminator
+            for t_3 in range(train_arguments.num_student_iters):
+                z = uniform([self.batch_size, self.noise_dim], dtype=float64)
+
+                sample = expand_dims(category_samples.sample(self.batch_size), axis=1)
+                # print("category_stu", sample.shape)
+
+                with GradientTape() as stu_tape:
+                    fake = self.generator(concat([z, sample], 1))
+                    # print('fake_stu', fake.shape)
+
+                    predictions, clean_votes = self._pate_voting(
+                        concat([fake, sample], 1), self.t_discriminators, train_arguments.lap_scale)
+                    # print("noisy_labels", predictions.shape, "clean_votes", clean_votes.shape)
+                    outputs = self.s_discriminator(concat([fake, sample], 1))
+
+                    # update the moments
+                    alpha = alpha + self._moments_acc(self.n_teachers, clean_votes, train_arguments.lap_scale, l_list)
+                    # print("final_alpha", alpha)
+
+                    stu_loss = cross_entropy(predictions, outputs)
+                    gradients_of_stu = stu_tape.gradient(stu_loss, self.s_discriminator.trainable_variables)
+                    # print(gradients_of_stu)
+
+                    disc_opt_stu.apply_gradients(zip(gradients_of_stu, self.s_discriminator.trainable_variables))
+
+            # train the generator
+            z = uniform([self.batch_size, self.noise_dim], dtype=float64)
+
+            sample_g = expand_dims(category_samples.sample(self.batch_size), axis=1)
+
+            with GradientTape() as gen_tape:
+                fake = self.generator(concat([z, sample_g], 1))
+                output = self.s_discriminator(concat([fake, sample_g], 1))
+
+                loss_gen = cross_entropy(ones_like(output), output)
+                gradients_of_generator = gen_tape.gradient(loss_gen, self.generator.trainable_variables)
+                generator_optimizer.apply_gradients(zip(gradients_of_generator, self.generator.trainable_variables))
+
+            # Calculate the current privacy cost
+            epsilon = min((alpha - log(train_arguments.delta)) / l_list)
+            if steps % train_arguments.sample_interval == 0:
+                print("Step : ", steps, "Loss SD : ", stu_loss, "Loss G : ", loss_gen, "Epsilon : ", epsilon)
+
+            steps += 1
+        # self.generator.summary()
+
+    def _pate_voting(self, data, netTD, lap_scale):
+        # TODO: Validate the logic against original article
+        ## Faz os votos dos teachers (1/0) netTD para cada record em data e guarda em results
+        results = zeros([len(netTD), self.batch_size], dtype=int64)
+        # print(results)
+        for i in range(len(netTD)):
+            output = netTD[i](data, training=True)
+            pred = transpose(cast((output > 0.5), int64))
+            # print(pred)
+            results = tensor_scatter_nd_update(results, constant([[i]]), pred)
+            # print(results)
+
+        #guarda o somatorio das probabilidades atribuidas por cada disc a cada record (valores entre 0 e len(netTD))
+        clean_votes = expand_dims(cast(reduce_sum(results, 0), dtype=float64), 1)
+        # print("clean_votes",clean_votes)
+        noise_sample = distributions.Laplace(loc=0, scale=1/lap_scale).sample(clean_votes.shape)
+        # print("noise_sample", noise_sample)
+        noisy_results = clean_votes + cast(noise_sample, float64)
+        noisy_labels = cast((noisy_results > len(netTD)/2), float64)
+
+        return noisy_labels, clean_votes
+
+
+class Discriminator(Model):
+    def __init__(self, batch_size):
+        self.batch_size = batch_size
+
+    def build_model(self, input_shape, dim):
+        input = Input(shape=input_shape, batch_size=self.batch_size)
+        x = Dense(dim * 4)(input)
+        x = ReLU()(x)
+        x = Dense(dim * 2)(x)
+        x = Dense(1)(x)
+        return Model(inputs=input, outputs=x)
+
+
+class Generator(Model):
+    def __init__(self, batch_size):
+        self.batch_size = batch_size
+
+    def build_model(self, input_shape, dim, data_dim, processor_info: Optional[NamedTuple] = None):
+        input = Input(shape=input_shape, batch_size = self.batch_size)
+        x = Dense(dim)(input)
+        x = ReLU()(x)
+        x = Dense(dim * 2)(x)
+        x = Dense(data_dim)(x)
+        if processor_info:
+            x = ActivationInterface(processor_info, 'ActivationInterface')(x)
+        return Model(inputs=input, outputs=x)