diff --git a/Dockerfile b/Dockerfile index 88cface8..7d9d039f 100644 --- a/Dockerfile +++ b/Dockerfile @@ -30,10 +30,13 @@ ADD configs configs ADD fltk fltk -# Update relevant runtime configuration for experiment -COPY cloud_configs/cloud_experiment.yaml configs/cloud_config.yaml + # Install newest version of library RUN python3 -m setup install # Expose the container's port to the host OS EXPOSE 5000 + + +# Update relevant runtime configuration for experiment +COPY cloud_configs/cloud_experiment.yaml configs/cloud_config.yaml \ No newline at end of file diff --git a/charts/federator/templates/fl-server-pod.yaml b/charts/federator/templates/fl-server-pod.yaml index e8c0f3e6..3b88db45 100644 --- a/charts/federator/templates/fl-server-pod.yaml +++ b/charts/federator/templates/fl-server-pod.yaml @@ -15,7 +15,7 @@ spec: - -m - fltk - poison - - configs/cloud_experiment.yaml + - configs/cloud_config.yaml - --rank=0 env: - name: MASTER_PORT diff --git a/cloud_configs/cloud_experiment.yaml b/cloud_configs/cloud_experiment.yaml index 566ab044..ec24c498 100644 --- a/cloud_configs/cloud_experiment.yaml +++ b/cloud_configs/cloud_experiment.yaml @@ -29,3 +29,7 @@ poison: type: "flip" config: - 5: 3 +antidote: + type: "clustering" + f: 0 + k: 1 \ No newline at end of file diff --git a/configs/local_experiment.yaml b/configs/local_experiment.yaml index 7a660b87..4786295a 100644 --- a/configs/local_experiment.yaml +++ b/configs/local_experiment.yaml @@ -1,5 +1,5 @@ # Experiment configuration -total_epochs: 1 +total_epochs: 35 epochs_per_cycle: 1 wait_for_clients: true net: Cifar10CNN @@ -13,7 +13,7 @@ cuda: false experiment_prefix: 'experiment_single_machine' output_location: 'output' tensor_board_active: true -clients_per_round: 2 +clients_per_round: 3 system: federator: # Use the SERVICE provided by the fl-server to connect @@ -21,7 +21,7 @@ system: # Default NIC is eth0 nic: 'eth0' clients: - amount: 2 + amount: 3 # For a simple config is provided in configs/poison.example.yaml poison: seed: 420 @@ -30,5 +30,9 @@ poison: type: "flip" config: - 5: 3 +antidote: + type: "clustering" + f: 0 + k: 1 diff --git a/fltk/datasets/distributed/cifar10.py b/fltk/datasets/distributed/cifar10.py index 58fb5d63..f4b5376b 100644 --- a/fltk/datasets/distributed/cifar10.py +++ b/fltk/datasets/distributed/cifar10.py @@ -2,7 +2,6 @@ from torch.utils.data import DataLoader import logging -from memory_profiler import profile from torch.utils.data import DataLoader from torchvision import datasets from torchvision import transforms diff --git a/fltk/federator.py b/fltk/federator.py index 4ee4cef3..cbd6307f 100644 --- a/fltk/federator.py +++ b/fltk/federator.py @@ -85,7 +85,7 @@ def __init__(self, client_id_triple, num_epochs=3, config: BareConfig = None, at self.attack = attack logging.info(f'Federator with attack {attack}') self.antidote = antidote - logging.info(f'Fedetrator with antidote {antidote}') + logging.info(f'Federator with antidote {antidote}') self.log_rref = log_rref self.num_epoch = num_epochs @@ -236,7 +236,10 @@ def remote_run_epoch(self, epochs, ratio=None, store_grad=False): self.epoch_counter) client_weights.append(weights) - updated_model = self.antidote.process_gradients(client_weights, epoch = self.epoch_counter) + # TODO: Make sure that we keep track of whose gradient we are dealing with + updated_model = self.antidote.process_gradients(client_weights, epoch=self.epoch_counter, + clients=selected_clients, + model=self.test_data.net.state_dict()) self.test_data.net.load_state_dict(updated_model) # test global model logging.info("Testing on global test set") @@ -277,7 +280,7 @@ def save_epoch_data(self, ratio=None): def ensure_path_exists(self, path): Path(path).mkdir(parents=True, exist_ok=True) - def run(self, ratios=[0.12, 0.18, 0.0]): + def run(self, ratios=[0.12, 0.18, 0.6, 0.0]): """ Main loop of the Federator :return: @@ -324,6 +327,7 @@ def run(self, ratios=[0.12, 0.18, 0.0]): self.client_reset_model() # Reset dataloader, etc. for next experiment self.set_data() + self.antidote.save_data_and_reset(rat) logging.info(f'Federator is stopping') diff --git a/fltk/strategy/antidote.py b/fltk/strategy/antidote.py index 96fd7a37..9f16a6ca 100644 --- a/fltk/strategy/antidote.py +++ b/fltk/strategy/antidote.py @@ -1,16 +1,15 @@ +import logging from abc import abstractmethod, ABC + import numpy as np -import torch +from sklearn.cluster import KMeans +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler -from fltk.client import Client -from fltk.nets.util.utils import flatten_params from fltk.strategy.util.antidote import calc_krum_scores from fltk.util.base_config import BareConfig from fltk.util.fed_avg import average_nn_parameters -from sklearn.cluster import KMeans -from sklearn.decomposition import PCA -from sklearn.preprocessing import StandardScaler class Antidote(ABC): @@ -21,6 +20,10 @@ def __init__(self): def process_gradients(self, gradients, **kwargs): pass + def save_data_and_reset(self, ratio, iteration=0): + pass + + class DummyAntidote(Antidote): def __init__(self, cfg: BareConfig): @@ -30,6 +33,7 @@ def __init__(self, cfg: BareConfig): def process_gradients(self, gradients, **kwargs): return average_nn_parameters(gradients) + class MultiKrumAntidote(Antidote): def __init__(self, cfg: BareConfig, **kwargs): @@ -50,81 +54,151 @@ def process_gradients(self, gradients, **kwargs): class ClusterAntidote(Antidote): - @staticmethod - def ema(s_t_prev, value, t, rho, bias_correction = True): - s_t = rho * s_t_prev + (1 - rho) * value + def ema(self, s_t_prev, value, t, bias_correction=True): + """ + Exponential Moving Average, with bias correction by default. + @param s_t_prev: + @type s_t_prev: + @param value: + @type value: + @param t: + @type t: + @param bias_correction: + @type bias_correction: + @return: + @rtype: + """ + s_t = self.rho_ * s_t_prev + (1 - self.rho_) * value s_t_hat = None if bias_correction: - s_t_hat = s_t / (1.0 - rho**(t + 1)) + s_t_hat = s_t / (1.0 - self.rho_ ** (t + 1)) return s_t_hat if bias_correction else s_t def __init__(self, cfg: BareConfig, **kwargs): - Antidote.__init__(self) + super(ClusterAntidote, self).__init__() + # Needed for KRUM/Multi-KRUM for testing purposes. self.f = cfg.get_antidote_f_value() self.k = cfg.get_antidote_k_value() self.past_gradients = np.array([]) - # TODO: Not hardcode this for cifar10 - self.class_targeted = np.zeros((10, cfg.epochs)) - # Rho for this round poisoned - self.rho_1 = 0.5 + self.logger = logging.getLogger() # Rho for this class poisoned - self.rho_1 = 0.75 + self.rho_ = 0.75 self.max_epoch = 130 self.num_classes = 10 + self.offset = 20 + + # Logging information for testing only + self.krum_proposed = list() + self.selected_updates = list() + self.cheating_client = dict() + self.class_targeted = np.zeros((10, cfg.epochs + 2)) + def process_gradients(self, gradients, **kwargs): """ Function which returns the average of the k gradient with the lowest score. """ epoch_indx = kwargs['epoch'] + clients_round = kwargs['clients'] + model = kwargs['model'] + cur_last = list(model.values())[-2].numpy() # First 10 epochs we effectively don't do much if epoch_indx > 10: - new_connected_grads = [next(reversed(gradient.values())).numpy() for gradient in gradients] - self.past_gradients = np.stack([self.past_gradients] + new_connected_grads) + # Store gradients + new_connected_grads = [list(gradient.values())[-2].numpy() - cur_last for gradient in gradients] + # The array may be empty + if self.past_gradients.size: + self.past_gradients = np.vstack([self.past_gradients] + new_connected_grads) + else: + self.past_gradients = np.vstack(new_connected_grads) # If collected enough data, we continue to the next round - if epoch_indx > 20: - trusty_indices = self.target_malicious(gradients, epoch_indx) + if epoch_indx > self.offset: + trusty_indices = self.target_malicious(gradients, epoch_indx, clients_round) return average_nn_parameters([gradients[indx] for indx in trusty_indices]) return average_nn_parameters(gradients) - def target_malicious(self, gradients, epoch_indx): + def target_malicious(self, gradients, epoch_indx, clients_round): truthy_gradient = np.zeros((self.num_classes, len(gradients)), dtype=bool) + krum_scores = calc_krum_scores(gradients, int(np.ceil(1/3 * len(gradients)))) + # Take the index of smallest client. + most_likely_good_index = np.argmin(krum_scores) + most_likely_good = clients_round[np.argmin(krum_scores)].name for cls in range(self.num_classes): # Slice to get only the rows corresponding the the output node. sub_sample = self.past_gradients[cls::self.num_classes] - clf = KMeans(2) - scaler = StandardScaler() - fitter = PCA(n_components=2) - scaled_param_diff = scaler.fit_transform(sub_sample) - dim_reduced_gradients = fitter.fit_transform(scaled_param_diff) - classified = clf.fit_transform(dim_reduced_gradients) + classified = self.unsupervised_classification(sub_sample) # If total is roughly 50/50 then unlikely to be poisoned. Else likely to be poisoned cluster_split = np.average(classified) - if 0.4 * epoch_indx * len(gradients) < cluster_split < 0.6 * len(gradients): + self.logger.info(f"Cluster division: {cluster_split}") + if 1 / 3 < cluster_split < 2 / 3: # Roughly 50/50 divided, so we assume valid updates. # As such, we don't need to perform KRUM, as the distribution over the two clusters # is arbitrary. Hence, we cannot distill much information from the assignment to one of the # two clusters. + + # Use 0 as estimate, because we suspect that the class is _not_ targeted. + self.class_targeted[cls, epoch_indx + 1] = self.ema(self.class_targeted[cls, epoch_indx], 0, + epoch_indx - self.offset) + # Broadcast True ot the truthy_gradient matrix truthy_gradient[cls] = True else: - krum_scores = calc_krum_scores(gradients) - most_likely_good = np.argmax(krum_scores) - # Get the label assigned to the 'krum' vector, either 1/0 - estimated_cluster = classified[-(len(gradients) - most_likely_good)] + # Use 0 as estimate, because we suspect that the class _is_ targeted. + self.class_targeted[cls, epoch_indx + 1] = self.ema(self.class_targeted[cls, epoch_indx], 1, + epoch_indx - self.offset) + biggest_cluster = 0 if cluster_split < 0.5 else 1 # Boolean array to indicate which belong to the same cluster. - truthy_gradient[cls] = classified[-len(gradients):] == estimated_cluster + truthy_gradient[cls] = (classified[-len(gradients):] == classified[-len(gradients) + most_likely_good_index]) + self.logger.info(f"Biggest: {biggest_cluster}, KRUM cluster: {classified[-len(gradients) + most_likely_good_index]}") # Only select the gradients that we suspect that are unaffected # Take row-wise and, as such only a column that has only 'TRUE', will be selected using # the argwhere, because True evaluates to True. - return np.argwhere(truthy_gradient) + truthy_gradient_reduced = np.prod(truthy_gradient, axis=0) + for indx, truthy in enumerate(truthy_gradient_reduced): + suspect_cheating = clients_round[indx].name + previous_array = self.cheating_client.get(suspect_cheating, [0]) + previous_array.append(self.ema(previous_array[-1], 1 if truthy != 1 else 0, len(previous_array))) + self.cheating_client[suspect_cheating] = previous_array + selected_grads = np.argwhere(truthy_gradient_reduced == 1).reshape(-1) + + self.krum_proposed.append(most_likely_good) + # Keep track of the gradients updates that we selected. + self.selected_updates.append([clients_round[indx].name for indx in selected_grads]) + self.logger.info(f"KRUM: {most_likely_good}, clustered: {selected_grads}") + self.logger.info(f"Suspicion classes: {self.class_targeted[:, epoch_indx + 1]}") + self.logger.info(f"Suspicion clients: {[(k, v[-1]) for k, v in self.cheating_client.items()]}") + return selected_grads + + def unsupervised_classification(self, sub_sample): + clf = KMeans(2) + scaler = StandardScaler() + fitter = PCA(n_components=2) + scaled_param_diff = scaler.fit_transform(sub_sample) + dim_reduced_gradients = fitter.fit_transform(scaled_param_diff) + classified = clf.fit_predict(dim_reduced_gradients) + return classified + + def save_data_and_reset(self, ratio, iteration=0): + data = { + "selected_updates": self.selected_updates, + "cheating_clients": self.cheating_client, + "targeted_classes": self.class_targeted, + "krum_proposal": self.krum_proposed, + "gradients": self.past_gradients + } + np.save(f'./output/cluster_antidote_{ratio}_{iteration}.npy', data) + self.selected_updates = list() + self.cheating_client = dict() + self.class_targeted = np.zeros_like(self.class_targeted) + self.krum_proposed = list() + self.past_gradients = np.array([]) def create_antidote(cfg: BareConfig, **kwargs) -> Antidote: assert cfg is not None if cfg.antidote is None: return DummyAntidote(cfg) - medicine_cabinet = {'dummy': DummyAntidote, 'multikrum': MultiKrumAntidote, 'cluster': ClusterAntidote} + medicine_cabinet = {'dummy': DummyAntidote, 'multikrum': MultiKrumAntidote, 'clustering': ClusterAntidote} antidote_class = medicine_cabinet.get(cfg.get_antidote_type(), None) diff --git a/notebooks/convert_experiment_data.ipynb b/notebooks/convert_experiment_data.ipynb index 039fafe8..ddabbe44 100644 --- a/notebooks/convert_experiment_data.ipynb +++ b/notebooks/convert_experiment_data.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 53, + "execution_count": 58, "id": "efc3c88d", "metadata": {}, "outputs": [ @@ -10,45 +10,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pandas in ./Documents/CSE/MSc/year/1/Q4/CS4245/repo/BMT/venv/lib/python3.9/site-packages (1.2.4)\n", - "Requirement already satisfied: numpy in ./Documents/CSE/MSc/year/1/Q4/CS4245/repo/BMT/venv/lib/python3.9/site-packages (1.19.5)\n", - "Requirement already satisfied: python-dateutil>=2.7.3 in ./Documents/CSE/MSc/year/1/Q4/CS4245/repo/BMT/venv/lib/python3.9/site-packages (from pandas) (2.8.1)\n", - "Requirement already satisfied: pytz>=2017.3 in ./Documents/CSE/MSc/year/1/Q4/CS4245/repo/BMT/venv/lib/python3.9/site-packages (from pandas) (2021.1)\n", - "Requirement already satisfied: six>=1.5 in ./Documents/CSE/MSc/year/1/Q4/CS4245/repo/BMT/venv/lib/python3.9/site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n", - "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 21.1.2 is available.\n", - "You should consider upgrading via the '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4245/repo/BMT/venv/bin/python -m pip install --upgrade pip' command.\u001b[0m\n" + "0 [0.32, 0.40277778, 0.52941176, 0.25, nan, 0.46...\n", + "1 [0.46808511, 0.39007092, 0.90909091, 0.1923076...\n", + "2 [0.46268657, 0.35802469, 0.39240506, 0.2745098...\n", + "3 [0.39252336, 0.3875, 0.31481481, 0.3030303, 0....\n", + "4 [0.51612903, 0.57, 0.30769231, 0.27737226, 0.2...\n", + " ... \n", + "195 [0.87096774, 0.95294118, 0.576, 0.71428571, 0....\n", + "196 [0.87628866, 0.94186047, 0.77027027, 0.75, 0.7...\n", + "197 [0.80555556, 0.90217391, 0.70114943, 0.7012987...\n", + "198 [0.84466019, 0.93258427, 0.66666667, 0.7215189...\n", + "199 [0.85576923, 0.94117647, 0.75949367, 0.6867469...\n", + "Name: class_precision, Length: 2000, dtype: object\n" ] } ], "source": [ - "!pip install pandas numpy\n", + "\n", "from io import StringIO\n", "import pandas as pd\n", "import numpy as np\n", - "from pathlib import Path" + "from pathlib import Path\n", + "print(data.class_precision)" ] }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 62, "id": "231acd9e", "metadata": {}, - "outputs": [], - "source": [ - "path = \"/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/charts/extractor/fig8/output_0.0/\"\n", - "\n", - "def mapper(x: str) -> np.array:\n", - " st = ','.join(x.split())\n", - " return np.genfromtxt(StringIO(st[1:-2]), delimiter=',')\n", - "\n", - "data = pd.concat([pd.read_csv(client, converters={'class_precision': mapper, 'class_recall': mapper}) for client in Path(path).rglob('*.csv')])" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "7b6d2327", - "metadata": {}, "outputs": [ { "data": { @@ -263,15 +253,251 @@ "[2000 rows x 9 columns]" ] }, - "execution_count": 81, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "path = \"/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/charts/extractor/fig8/output_0.0/\"\n", + "\n", + "def mapper(x: str) -> np.array:\n", + " st = ','.join(x.split())\n", + " return np.genfromtxt(StringIO(st[1:-2]), delimiter=',')\n", + "\n", + "data = pd.concat([pd.read_csv(client, converters={'class_precision': mapper, 'class_recall': mapper}) for client in Path(path).rglob('*.csv')])\n", "data" ] }, + { + "cell_type": "code", + "execution_count": 75, + "id": "7b6d2327", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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..............................
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" + ], + "text/plain": [ + " epoch_id client_id duration_train duration_test loss_train \\\n", + "3 1 client3 49852 3028 2.147378 \n", + "13 2 client3 43976 2914 2.055118 \n", + "23 3 client3 44172 2872 2.081944 \n", + "33 4 client3 43971 2851 2.061278 \n", + "43 5 client3 44330 2730 2.008247 \n", + "... ... ... ... ... ... \n", + "1953 196 client3 43801 2963 1.585445 \n", + "1963 197 client3 44140 2844 1.593065 \n", + "1973 198 client3 43470 2909 1.600651 \n", + "1983 199 client3 44220 2961 1.695308 \n", + "1993 200 client3 44299 2863 1.600772 \n", + "\n", + " accuracy loss class_precision \\\n", + "3 34.763476 121.799125 0.32 \n", + "13 37.953795 118.671685 0.468085 \n", + "23 40.044004 117.759390 0.462687 \n", + "33 39.493949 117.232983 0.392523 \n", + "43 42.684268 115.907113 0.516129 \n", + "... ... ... ... \n", + "1953 81.408141 93.820704 0.804878 \n", + "1963 81.078108 93.799207 0.764706 \n", + "1973 82.618262 93.303663 0.770115 \n", + "1983 81.958196 93.492339 0.802326 \n", + "1993 81.848185 93.687668 0.784091 \n", + "\n", + " class_recall \n", + "3 [0.48780488, 0.725, 0.08737864, 0.03448276, 0.... \n", + "13 [0.26829268, 0.6875, 0.09708738, 0.05747126, 0... \n", + "23 [0.37804878, 0.725, 0.30097087, 0.32183908, 0.... \n", + "33 [0.51219512, 0.775, 0.33009709, 0.34482759, 0.... \n", + "43 [0.3902439, 0.7125, 0.15533981, 0.43678161, 0.... \n", + "... ... \n", + "1953 [0.80487805, 0.9375, 0.77669903, 0.71264368, 0... \n", + "1963 [0.79268293, 0.95, 0.76699029, 0.64367816, 0.8... \n", + "1973 [0.81707317, 0.95, 0.75728155, 0.68965517, 0.8... \n", + "1983 [0.84146341, 0.925, 0.74757282, 0.72413793, 0.... \n", + "1993 [0.84146341, 0.925, 0.7184466, 0.66666667, 0.7... \n", + "\n", + "[200 rows x 9 columns]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = data.explode('class_precision').sort_index(ascending=False, kind='mergesort').groupby(['epoch_id', 'client_id']).nth(0).reset_index()\n", + "test[test.client_id == 'client3']" + ] + }, { "cell_type": "code", "execution_count": 94, diff --git a/notebooks/gradient-PCA.ipynb b/notebooks/gradient-PCA.ipynb index ede417b0..44a8349e 100644 --- a/notebooks/gradient-PCA.ipynb +++ b/notebooks/gradient-PCA.ipynb @@ -9,10 +9,11 @@ "import re\n", "from collections import OrderedDict\n", "from pathlib import Path\n", - "\n", + "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import sklearn.decomposition\n", "import torch\n", + "import torch.nn.functional as F\n", "from natsort import natsorted\n", "from sklearn.preprocessing import StandardScaler\n", "\n", @@ -21,17 +22,6 @@ "from fltk.util.base_config import BareConfig" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 2, @@ -52,33 +42,6 @@ " return torch.stack(directories)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 3, @@ -97,65 +60,6 @@ " return scaler.fit_transform(gradients)" ] }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'directories' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)", - "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mdirectories\u001B[0m \u001B[0;34m=\u001B[0m 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}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'directories' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)", - "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mtest\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdirectories\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m551078\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;36m552358\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mview\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m1300\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m1280\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 2\u001B[0m \u001B[0mfitter\u001B[0m \u001B[0;34m=\u001B[0m 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= directories[:, 551078:552358].view(1300, 1280)\n", - "fitter = sklearn.decomposition.PCA(n_components=2)\n", - "\n", - "scaled_param_diff = apply_standard_scaler(test)\n", - "dim_reduced_gradients = fitter.fit_transform(scaled_param_diff)\n", - "for indx in range(1300):\n", - " plt.scatter(dim_reduced_gradients[indx, 0], dim_reduced_gradients[indx, 1], color='r' if poisoned[indx] else 'b')\n", - "plt.show()" - ] - }, { "cell_type": "code", "execution_count": 4, @@ -178,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" @@ -186,11 +90,6 @@ }, "outputs": [], "source": [ - "torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "\n", - "\n", "\n", "def flatten_params(parameters):\n", " \"\"\"\n", @@ -242,365 +141,384 @@ }, "outputs": [], "source": [ - "import numpy as np\n", + "\n", "\n", "model = Cifar10CNN()\n", "default_model_path = f\"../default_models/Cifar10CNN.model\"\n", "model.load_state_dict(torch.load(default_model_path))\n", - "flattened_default = flatten_params(model.state_dict())\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([552368])" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "restored = flattened_default['params'].view(-1) + test" + "flattened_default = flatten_params(model.state_dict())" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "fail\n", - "fail\n", - "fail\n", - 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"DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n", + "DEBUG:matplotlib.backends.backend_pdf:Assigning font /b'F1' = '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n", + "DEBUG:matplotlib.backends.backend_pdf:Embedding font /home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.\n", + "DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n", + "DEBUG:matplotlib.backends.backend_pdf:Assigning font /b'F1' = '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n", + "DEBUG:matplotlib.backends.backend_pdf:Embedding font /home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.\n", + "DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n", + "DEBUG:matplotlib.backends.backend_pdf:Assigning font /b'F1' = '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n", + "DEBUG:matplotlib.backends.backend_pdf:Embedding font /home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.\n", + "DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n", + "DEBUG:matplotlib.backends.backend_pdf:Assigning font /b'F1' = '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n" ] - } - ], - "source": [ - "recovered_params, state_dict = recover_flattened(restored.unsqueeze(-1), flattened_default['indices'], model)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "recovered_model = model.load_state_dict(state_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ + }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Welcome to client test\n", - "WARNING:root:Could not find model: default_models/Cifar10CNN.model\n" + "DEBUG:matplotlib.backends.backend_pdf:Embedding font /home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.\n", + "DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n", + "DEBUG:matplotlib.backends.backend_pdf:Assigning font /b'F1' = '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n", + "DEBUG:matplotlib.backends.backend_pdf:Embedding font /home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.\n", + "DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n", + "DEBUG:matplotlib.backends.backend_pdf:Assigning font /b'F1' = '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n", + "DEBUG:matplotlib.backends.backend_pdf:Embedding font /home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.\n", + "DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n", + ":5: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " f, ax = plt.subplots(nrows=1, ncols=3, figsize=(18, 6), sharex=True, sharey=True)\n", + "DEBUG:matplotlib.backends.backend_pdf:Assigning font /b'F1' = '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n", + "DEBUG:matplotlib.backends.backend_pdf:Embedding font /home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.\n", + "DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n", + "DEBUG:matplotlib.backends.backend_pdf:Assigning font /b'F1' = '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n", + "DEBUG:matplotlib.backends.backend_pdf:Embedding font /home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.\n", + "DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n", + "DEBUG:matplotlib.backends.backend_pdf:Assigning font /b'F1' = '/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n", + "DEBUG:matplotlib.backends.backend_pdf:Embedding font /home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/venv/lib/python3.9/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf.\n", + "DEBUG:matplotlib.backends.backend_pdf:Writing TrueType font.\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Client test is stopping\n" - ] - } - ], - "source": [ - "test_data = Client(\"test\", None, 1, 2, BareConfig())" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:root:Instantiated CIFAR10 train data, with pill: None\n", - "DEBUG:root:Loading 'distributed' CIFAR10 train data\n" - ] + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/fltk/client.py\", line 150, in init_dataloader\n", - " self.dataset = self.args.DistDatasets[self.args.dataset_name](self.args, pill)\n", - " File \"/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/fltk/datasets/distributed/cifar10.py\", line 17, in __init__\n", - " self.init_train_dataset()\n", - " File \"/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/fltk/datasets/distributed/cifar10.py\", line 36, in init_train_dataset\n", - " self.train_sampler = get_sampler(self.train_dataset, self.args)\n", - " File \"/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/fltk/strategy/data_samplers.py\", line 244, in get_sampler\n", - " \"Using {} sampler method, with args: {}\".format(method, args.get_sampler_args()))\n", - " File \"/home/jeroen/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/fltk/util/base_config.py\", line 164, in get_sampler_args\n", - " return tuple(self.data_sampler_args)\n", - "TypeError: 'NoneType' object is not iterable\n", - "\n", - "Done with init\n" - ] - } - ], - "source": [ - "test_data.init_dataloader()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'get_test_loader'", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mAttributeError\u001B[0m Traceback (most recent call last)", - "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[0mtest_data\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnet\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mtest_data\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtest\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m", - "\u001B[0;32m~/Documents/CSE/MSc/year/1/Q4/CS4290/repo/fltk-testbed-gr-30/fltk/client.py\u001B[0m in 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\n", 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\n", "text/plain": [ - "tensor([ 0.0784, 0.0881, 0.0798, -0.0379, 0.0801, 0.0417, 0.0034, -0.0510,\n", - " -0.0166, -0.0446, -0.0645, -0.0369, 0.0494, -0.0604, -0.0321, 0.0512,\n", - " 0.0566, 0.0195, -0.0848, -0.0778, -0.0247, -0.0786, -0.0587, 0.0727,\n", - " 0.0637, 0.0688, 0.0395, -0.0222, 0.0549, 0.0427, -0.0211, 0.0285,\n", - " -0.0579, -0.0493, 0.0550, -0.0144, -0.0426, -0.0838, 0.0051, 0.0074,\n", - " 0.0426, 0.0815, 0.0274, 0.0638, -0.0438, -0.0355, -0.0100, -0.0620,\n", - " 0.0215, 0.0475, -0.0516, -0.0041, 0.0599, -0.0819, -0.0853, -0.0008,\n", - " -0.0352, -0.0089, 0.0825, -0.0147, 0.0709, -0.0455, 0.0345, -0.0150,\n", - " 0.0276, 0.0691, 0.0683, 0.0294, 0.0435, 0.0406, -0.0534, -0.0046,\n", - " 0.0113, 0.0083, -0.0107, -0.0324, 0.0732, -0.0201, 0.0077, -0.0301,\n", - " 0.0468, -0.0851, -0.0078, -0.0583, 0.0137, 0.0001, -0.0507, 0.0766,\n", - " 0.0161, -0.0227, 0.0079, 0.0530, -0.0514, 0.0597, 0.0764, -0.0182,\n", - " -0.0838, -0.0694, 0.0323, -0.0560, -0.0086, -0.0132, 0.0401, -0.0221,\n", - " 0.0196, -0.0126, 0.0458, -0.0372, -0.0163, 0.0072, 0.0468, -0.0200,\n", - " -0.0516, 0.0005, -0.0471, -0.0644, 0.0162, 0.0244, 0.0873, 0.0393,\n", - " 0.0015, 0.0733, 0.0783, 0.0022, -0.0630, -0.0849, 0.0165, -0.0127])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Last fully connected layer, connected to target class\n", - "target_class = 5\n", - "flattened_default['params'][range(*(flattened_default['indices'][-2]))].view(10, 128)[target_class]" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, { "data": { + "image/png": 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\n", "text/plain": [ - "torch.Size([10, 128])" + "
" ] }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "flattened_default['indices'][-2]\n", - "torch.nn.Linear(128, 10).weight.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, { - "ename": "TypeError", - "evalue": "list indices must be integers or slices, not tuple", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", - "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[1;32m 2\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0msklearn\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msvm\u001B[0m \u001B[0;32mimport\u001B[0m 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\u001B[0;36m6\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 5\u001B[0m \u001B[0mfitter\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0msklearn\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdecomposition\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mPCA\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mn_components\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 6\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n", - "\u001B[0;31mTypeError\u001B[0m: list indices must be integers or slices, not tuple" - ] - } - ], - "source": [ - "from sklearn.svm import OneClassSVM\n", - "from sklearn.cluster import KMeans\n", - "\n", - "poisoned_class = directories[:, 551078:552358].view(1300, 10, 128)[:, 6]\n", - "fitter = sklearn.decomposition.PCA(n_components=2)\n", - "\n", - "scaled_param_diff = apply_standard_scaler(poisoned_class)\n", - "dim_reduced_gradients = fitter.fit_transform(scaled_param_diff)\n", - "\n", - "clf = OneClassSVM('poly', degree=10)\n", - "clf.fit(dim_reduced_gradients)\n", - "\n", - "np.dot(clf.predict(dim_reduced_gradients), -1 * (poisoned * 2 - 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "from mlxtend.plotting import plot_decision_regions\n", - "\n", - "plot_decision_regions(X=dim_reduced_gradients, y=poisoned * 2 - 1, clf=clf, legend=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n", "def plot_dataset(directories, poisoned, ratio):\n", + " # The 0.0 fraction is a bit broken\n", + " n = min(len(directories), len(poisoned))\n", + " \n", " f, ax = plt.subplots(nrows=1, ncols=3, figsize=(18, 6), sharex=True, sharey=True)\n", " # Shift title to top\n", " f.suptitle(f\"PCA analysis gradients with {ratio} poisoned\")\n", @@ -608,19 +526,18 @@ " ax[1].set_xlabel('PCA dim 1')\n", " # Shared y axis label\n", " ax[0].set_ylabel('PCA dim 2')\n", - "\n", + " lim_poisoned = poisoned[:n]\n", " # For non-target/source class, source class, target class\n", " for col, indx in enumerate([0, 4, 6]):\n", - " test = directories[:, 551078:552358].view(1898, 10, 128)[:, indx]\n", + " test = directories[:n, 551078:552358].view(n, 10, 128)[:, indx]\n", " fitter = sklearn.decomposition.PCA(n_components=2)\n", "\n", " scaled_param_diff = apply_standard_scaler(test)\n", " dim_reduced_gradients = fitter.fit_transform(scaled_param_diff)\n", - "\n", - " ax[col].scatter(dim_reduced_gradients[poisoned.T, 0], dim_reduced_gradients[poisoned.T, 1],\n", + " ax[col].scatter(dim_reduced_gradients[lim_poisoned.T, 0], dim_reduced_gradients[lim_poisoned.T, 1],\n", " color='r',\n", " marker='x')\n", - " ax[col].scatter(dim_reduced_gradients[poisoned.T == False, 0], dim_reduced_gradients[poisoned.T == False, 1],\n", + " ax[col].scatter(dim_reduced_gradients[lim_poisoned.T == False, 0], dim_reduced_gradients[lim_poisoned.T == False, 1],\n", " color='b',\n", " marker='o', alpha=0.1)\n", "\n", @@ -636,13 +553,14 @@ "regex_3 = re.compile(\"client[926]\")\n", "\n", "first_ten_rounds = re.compile(\"\\/([0-9]|10)\\/\")\n", - "for regex, name in [(regex_1, \"0.1\")]:\n", + "for regex, name in [(regex_3, \"0.3\")]:\n", " grad_paths = list(Path(f\"../charts/extractor/fig8/output_{name}/gradient/\").rglob(\"**/*.pt\"))\n", " grad_paths = [str(path) for path in grad_paths if not any(first_ten_rounds.findall(str(path)))][:-1]\n", " grad_paths = natsorted(grad_paths)\n", " poisoned = np.array([any(regex.findall(path)) for path in grad_paths])\n", - " directories = load_gradients(grad_paths)\n", - "# plot_dataset(directories, poisoned, name)" + "# directories = load_gradients(grad_paths)\n", + " for x in range(1, 24):\n", + " plot_dataset(directories[(x-1)* 10:x*10], poisoned, name)" ] }, { @@ -880,15 +798,15 @@ "evalue": "Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)", "output_type": "error", "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", - "\u001B[0;32m\u001B[0m in \u001B[0;36m\u001B[0;34m\u001B[0m\n\u001B[1;32m 6\u001B[0m \u001B[0mclf\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdim_reduced_gradients\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 7\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 8\u001B[0;31m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msilhouette_score\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdim_reduced_gradients\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mclf\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpredict\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdim_reduced_gradients\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m", - 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Valid values are 2 \"\n\u001b[0m\u001b[1;32m 35\u001b[0m \"to n_samples - 1 (inclusive)\" % n_labels)\n\u001b[1;32m 36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)" ] } ], @@ -925,4 +843,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +}