120 KiB
120 KiB
In [1]:
#IMPORTS import numpy as np import random import tensorflow as tf import tensorflow.keras as kr import tensorflow.keras.backend as K from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense from tensorflow.keras.datasets import mnist import os import csv from scipy.spatial.distance import euclidean from sklearn.metrics import confusion_matrix from time import sleep from tqdm import tqdm import copy import numpy from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier import pandas as pd import matplotlib.pyplot as plt import math import seaborn as sns from numpy.random import RandomState import scipy as scp from sklearn.model_selection import train_test_split from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, LabelEncoder from keras.models import Sequential from keras.layers import Dense from keras import optimizers from keras.callbacks import EarlyStopping,ModelCheckpoint from keras.utils import to_categorical from keras import backend as K from itertools import product from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import roc_auc_score from sklearn.metrics import confusion_matrix from sklearn import mixture from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt %matplotlib inline
Using TensorFlow backend.
In [2]:
# Enter here the data set you want to explain (adult, activity, or synthatic) data_set = 'adult' # Enter here the numb er of peers you want in the experiments n_peers = 100
In [3]:
# the random state we will use in the experiments. It can be changed rs = RandomState(92)
In [4]:
# preprocessing adults data set if data_set == 'adult': #Load dataset into a pandas DataFrame adult_data = pd.read_csv('adult_data.csv', na_values='?') # Drop all records with missing values adult_data.dropna(inplace=True) adult_data.reset_index(drop=True, inplace=True) # Drop fnlwgt, not interesting for ML adult_data.drop('fnlwgt', axis=1, inplace=True) adult_data.drop('education', axis=1, inplace=True) # merging some similar features. adult_data['marital-status'].replace('Married-civ-spouse', 'Married', inplace=True) adult_data['marital-status'].replace('Divorced', 'Unmarried', inplace=True) adult_data['marital-status'].replace('Never-married', 'Unmarried', inplace=True) adult_data['marital-status'].replace('Separated', 'Unmarried', inplace=True) adult_data['marital-status'].replace('Widowed', 'Unmarried', inplace=True) adult_data['marital-status'].replace('Married-spouse-absent', 'Married', inplace=True) adult_data['marital-status'].replace('Married-AF-spouse', 'Married', inplace=True) adult_data = pd.concat([adult_data,pd.get_dummies(adult_data['income'], prefix='income')],axis=1) adult_data.drop('income', axis=1, inplace=True) obj_columns = adult_data.select_dtypes(['object']).columns adult_data[obj_columns] = adult_data[obj_columns].astype('category') # Convert numerics to floats and normalize num_columns = adult_data.select_dtypes(['int64']).columns adult_data[num_columns] = adult_data[num_columns].astype('float64') for c in num_columns: #adult[c] -= adult[c].mean() #adult[c] /= adult[c].std() adult_data[c] = (adult_data[c] - adult_data[c].min()) / (adult_data[c].max()-adult_data[c].min()) # 'workclass', 'marital-status', 'occupation', 'relationship' ,'race', 'gender', 'native-country' # adult_data['income'] = adult_data['income'].cat.codes adult_data['marital-status'] = adult_data['marital-status'].cat.codes adult_data['occupation'] = adult_data['occupation'].cat.codes adult_data['relationship'] = adult_data['relationship'].cat.codes adult_data['race'] = adult_data['race'].cat.codes adult_data['gender'] = adult_data['gender'].cat.codes adult_data['native-country'] = adult_data['native-country'].cat.codes adult_data['workclass'] = adult_data['workclass'].cat.codes num_columns = adult_data.select_dtypes(['int8']).columns adult_data[num_columns] = adult_data[num_columns].astype('float64') for c in num_columns: #adult[c] -= adult[c].mean() #adult[c] /= adult[c].std() adult_data[c] = (adult_data[c] - adult_data[c].min()) / (adult_data[c].max()-adult_data[c].min()) display(adult_data.info()) display(adult_data.head(10)) adult_data = adult_data.to_numpy() # splite the data to train and test datasets X_train, X_test, y_train, y_test = train_test_split(adult_data[:,:-2],adult_data[:,-2:], test_size=0.07, random_state=rs) # the names of the features names = ['age','workclass','educational-num','marital-status','occupation', 'relationship','race','gender','capital-gain','capital-loss','hours-per-week','native-country'] Features_number = len(X_train[0])
<class 'pandas.core.frame.DataFrame'> RangeIndex: 45222 entries, 0 to 45221 Data columns (total 14 columns): age 45222 non-null float64 workclass 45222 non-null float64 educational-num 45222 non-null float64 marital-status 45222 non-null float64 occupation 45222 non-null float64 relationship 45222 non-null float64 race 45222 non-null float64 gender 45222 non-null float64 capital-gain 45222 non-null float64 capital-loss 45222 non-null float64 hours-per-week 45222 non-null float64 native-country 45222 non-null float64 income_<=50K 45222 non-null uint8 income_>50K 45222 non-null uint8 dtypes: float64(12), uint8(2) memory usage: 4.2 MB
None
| age | workclass | educational-num | marital-status | occupation | relationship | race | gender | capital-gain | capital-loss | hours-per-week | native-country | income_<=50K | income_>50K | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.109589 | 0.333333 | 0.400000 | 1.0 | 0.461538 | 0.6 | 0.5 | 1.0 | 0.000000 | 0.0 | 0.397959 | 0.95 | 1 | 0 |
| 1 | 0.287671 | 0.333333 | 0.533333 | 0.0 | 0.307692 | 0.0 | 1.0 | 1.0 | 0.000000 | 0.0 | 0.500000 | 0.95 | 1 | 0 |
| 2 | 0.150685 | 0.166667 | 0.733333 | 0.0 | 0.769231 | 0.0 | 1.0 | 1.0 | 0.000000 | 0.0 | 0.397959 | 0.95 | 0 | 1 |
| 3 | 0.369863 | 0.333333 | 0.600000 | 0.0 | 0.461538 | 0.0 | 0.5 | 1.0 | 0.076881 | 0.0 | 0.397959 | 0.95 | 0 | 1 |
| 4 | 0.232877 | 0.333333 | 0.333333 | 1.0 | 0.538462 | 0.2 | 1.0 | 1.0 | 0.000000 | 0.0 | 0.295918 | 0.95 | 1 | 0 |
| 5 | 0.630137 | 0.666667 | 0.933333 | 0.0 | 0.692308 | 0.0 | 1.0 | 1.0 | 0.031030 | 0.0 | 0.316327 | 0.95 | 0 | 1 |
| 6 | 0.095890 | 0.333333 | 0.600000 | 1.0 | 0.538462 | 0.8 | 1.0 | 0.0 | 0.000000 | 0.0 | 0.397959 | 0.95 | 1 | 0 |
| 7 | 0.520548 | 0.333333 | 0.200000 | 0.0 | 0.153846 | 0.0 | 1.0 | 1.0 | 0.000000 | 0.0 | 0.091837 | 0.95 | 1 | 0 |
| 8 | 0.657534 | 0.333333 | 0.533333 | 0.0 | 0.461538 | 0.0 | 1.0 | 1.0 | 0.064181 | 0.0 | 0.397959 | 0.95 | 0 | 1 |
| 9 | 0.260274 | 0.000000 | 0.800000 | 0.0 | 0.000000 | 0.0 | 1.0 | 1.0 | 0.000000 | 0.0 | 0.397959 | 0.95 | 1 | 0 |
In [5]:
if data_set == 'synthatic': #generate the data X, y = make_classification(n_samples=1000000, n_features=10, n_redundant=3, n_repeated=2, #n_classes=3, n_informative=5, n_clusters_per_class=4, random_state=42) y = pd.DataFrame(data=y, columns=["y"]) y = pd.get_dummies(y['y'], prefix='y') y = y.to_numpy() X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.07, random_state=rs) # the names of the features names = ['X(0)','X(1)','X(2)','X(3)','X(4)','X(5)','X(6)','X(7)','X(8)','X(9)'] Features_number = len(X_train[0])
In [6]:
if data_set == 'activity': #Load dataset into a pandas DataFrame activity = pd.read_csv("activity_3_original.csv", sep=',') # drop some features that have non value in the majority of the samples to_drop = ['subject', 'timestamp', 'heart_rate','activityID'] activity.drop(axis=1, columns=to_drop, inplace=True) # prepare the truth activity = pd.concat([activity,pd.get_dummies(activity['motion'], prefix='motion')],axis=1) activity.drop('motion', axis=1, inplace=True) class_label = [ 'motion_n', 'motion_y'] predictors = [a for a in activity.columns.values if a not in class_label] for p in predictors: activity[p].fillna(activity[p].mean(), inplace=True) display(predictors) for p in predictors: activity[p] = (activity[p]-activity[p].min()) / (activity[p].max() - activity[p].min()) activity[p].astype('float32') activity = activity.to_numpy() X_train, X_test, y_train, y_test = train_test_split(activity[:,:-2],activity[:,-2:], test_size=0.07, random_state=rs) # the names of the features names = ['temp_hand','acceleration_16_x_hand', 'acceleration_16_y_hand','acceleration_16_z_hand','acceleration_6_x_hand', 'acceleration_6_y_hand','acceleration_6_z_hand','gyroscope_x_hand','gyroscope_y_hand', 'gyroscope_z_hand','magnetometer_x_hand','magnetometer_y_hand','magnetometer_z_hand', 'temp_chest','acceleration_16_x_chest','acceleration_16_y_chest','acceleration_16_z_chest','acceleration_6_x_chest', 'acceleration_6_y_chest','acceleration_6_z_chest','gyroscope_x_chest','gyroscope_y_chest','gyroscope_z_chest', 'magnetometer_x_chest','magnetometer_y_chest','magnetometer_z_chest','temp_ankle','acceleration_16_x_ankle', 'acceleration_16_y_ankle','acceleration_16_z_ankle','acceleration_6_x_ankle','acceleration_6_y_ankle', 'acceleration_6_z_ankle','gyroscope_x_ankle','gyroscope_y_ankle','gyroscope_z_ankle','magnetometer_x_ankle', 'magnetometer_y_ankle','magnetometer_z_ankle'] Features_number = len(X_train[0])
In [7]:
#begin federated earlystopping = EarlyStopping(monitor = 'val_loss', min_delta = 0.01, patience = 50, verbose = 1, baseline = 2, restore_best_weights = True) checkpoint = ModelCheckpoint('test.h8', monitor='val_loss', mode='min', save_best_only=True, verbose=1) model = Sequential() model.add(Dense(70, input_dim=Features_number, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(2, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy']) history = model.fit(X_train, y_train, epochs=2, validation_data=(X_test, y_test), callbacks = [checkpoint, earlystopping], shuffle=True)
Train on 42056 samples, validate on 3166 samples Epoch 1/2 42056/42056 [==============================] - ETA: 2:15 - loss: 0.7931 - accuracy: 0.28 - ETA: 5s - loss: 0.5651 - accuracy: 0.6988 - ETA: 3s - loss: 0.5165 - accuracy: 0.72 - ETA: 2s - loss: 0.4896 - accuracy: 0.74 - ETA: 2s - loss: 0.4646 - accuracy: 0.76 - ETA: 2s - loss: 0.4489 - accuracy: 0.77 - ETA: 1s - loss: 0.4412 - accuracy: 0.77 - ETA: 1s - loss: 0.4316 - accuracy: 0.78 - ETA: 1s - loss: 0.4258 - accuracy: 0.78 - ETA: 1s - loss: 0.4224 - accuracy: 0.79 - ETA: 1s - loss: 0.4158 - accuracy: 0.79 - ETA: 1s - loss: 0.4118 - accuracy: 0.79 - ETA: 1s - loss: 0.4073 - accuracy: 0.80 - ETA: 1s - loss: 0.4052 - accuracy: 0.80 - ETA: 1s - loss: 0.4025 - accuracy: 0.80 - ETA: 1s - loss: 0.3992 - accuracy: 0.80 - ETA: 1s - loss: 0.3983 - accuracy: 0.80 - ETA: 0s - loss: 0.3964 - accuracy: 0.80 - ETA: 0s - loss: 0.3930 - accuracy: 0.80 - ETA: 0s - loss: 0.3893 - accuracy: 0.81 - ETA: 0s - loss: 0.3881 - accuracy: 0.81 - ETA: 0s - loss: 0.3873 - accuracy: 0.81 - ETA: 0s - loss: 0.3857 - accuracy: 0.81 - ETA: 0s - loss: 0.3831 - accuracy: 0.81 - ETA: 0s - loss: 0.3811 - accuracy: 0.81 - ETA: 0s - loss: 0.3793 - accuracy: 0.81 - ETA: 0s - loss: 0.3784 - accuracy: 0.81 - ETA: 0s - loss: 0.3764 - accuracy: 0.81 - ETA: 0s - loss: 0.3756 - accuracy: 0.81 - ETA: 0s - loss: 0.3740 - accuracy: 0.82 - ETA: 0s - loss: 0.3726 - accuracy: 0.82 - 2s 41us/step - loss: 0.3720 - accuracy: 0.8217 - val_loss: 0.3452 - val_accuracy: 0.8370 Epoch 00001: val_loss improved from inf to 0.34516, saving model to test.h8 Epoch 2/2 42056/42056 [==============================] - ETA: 3s - loss: 0.5095 - accuracy: 0.84 - ETA: 1s - loss: 0.3444 - accuracy: 0.83 - ETA: 1s - loss: 0.3407 - accuracy: 0.84 - ETA: 1s - loss: 0.3383 - accuracy: 0.83 - ETA: 1s - loss: 0.3365 - accuracy: 0.84 - ETA: 1s - loss: 0.3374 - accuracy: 0.84 - ETA: 1s - loss: 0.3386 - accuracy: 0.84 - ETA: 1s - loss: 0.3357 - accuracy: 0.84 - ETA: 1s - loss: 0.3364 - accuracy: 0.84 - ETA: 1s - loss: 0.3353 - accuracy: 0.84 - ETA: 1s - loss: 0.3351 - accuracy: 0.84 - ETA: 1s - loss: 0.3368 - accuracy: 0.83 - ETA: 1s - loss: 0.3381 - accuracy: 0.83 - ETA: 1s - loss: 0.3396 - accuracy: 0.83 - ETA: 0s - loss: 0.3388 - accuracy: 0.83 - ETA: 0s - loss: 0.3384 - accuracy: 0.83 - ETA: 0s - loss: 0.3393 - accuracy: 0.83 - ETA: 0s - loss: 0.3390 - accuracy: 0.83 - ETA: 0s - loss: 0.3392 - accuracy: 0.83 - ETA: 0s - loss: 0.3400 - accuracy: 0.83 - ETA: 0s - loss: 0.3400 - accuracy: 0.83 - ETA: 0s - loss: 0.3410 - accuracy: 0.83 - ETA: 0s - loss: 0.3406 - accuracy: 0.83 - ETA: 0s - loss: 0.3415 - accuracy: 0.83 - ETA: 0s - loss: 0.3419 - accuracy: 0.83 - ETA: 0s - loss: 0.3421 - accuracy: 0.83 - ETA: 0s - loss: 0.3417 - accuracy: 0.83 - ETA: 0s - loss: 0.3410 - accuracy: 0.83 - ETA: 0s - loss: 0.3415 - accuracy: 0.83 - ETA: 0s - loss: 0.3419 - accuracy: 0.83 - ETA: 0s - loss: 0.3420 - accuracy: 0.83 - ETA: 0s - loss: 0.3411 - accuracy: 0.83 - ETA: 0s - loss: 0.3413 - accuracy: 0.83 - 2s 41us/step - loss: 0.3412 - accuracy: 0.8375 - val_loss: 0.3417 - val_accuracy: 0.8370 Epoch 00002: val_loss improved from 0.34516 to 0.34170, saving model to test.h8
In [8]:
#AUXILIARY METHODS FOR FEDERATED LEARNING # RETURN INDICES TO LAYERS WITH WEIGHTS AND BIASES def trainable_layers(model): return [i for i, layer in enumerate(model.layers) if len(layer.get_weights()) > 0] # RETURN WEIGHTS AND BIASES OF A MODEL def get_parameters(model): weights = [] biases = [] index = trainable_layers(model) for i in index: weights.append(copy.deepcopy(model.layers[i].get_weights()[0])) biases.append(copy.deepcopy(model.layers[i].get_weights()[1])) return weights, biases # SET WEIGHTS AND BIASES OF A MODEL def set_parameters(model, weights, biases): index = trainable_layers(model) for i, j in enumerate(index): model.layers[j].set_weights([weights[i], biases[i]]) # DEPRECATED: RETURN THE GRADIENTS OF THE MODEL AFTER AN UPDATE def get_gradients(model, inputs, outputs): """ Gets gradient of model for given inputs and outputs for all weights""" grads = model.optimizer.get_gradients(model.total_loss, model.trainable_weights) symb_inputs = (model._feed_inputs + model._feed_targets + model._feed_sample_weights) f = K.function(symb_inputs, grads) x, y, sample_weight = model._standardize_user_data(inputs, outputs) output_grad = f(x + y + sample_weight) w_grad = [w for i,w in enumerate(output_grad) if i%2==0] b_grad = [w for i,w in enumerate(output_grad) if i%2==1] return w_grad, b_grad # RETURN THE DIFFERENCE OF MODELS' WEIGHTS AND BIASES AFTER AN UPDATE # NOTE: LEARNING RATE IS APPLIED, SO THE UPDATE IS DIFFERENT FROM THE # GRADIENTS. IN CASE VANILLA SGD IS USED, THE GRADIENTS ARE OBTAINED # AS (UPDATES / LEARNING_RATE) def get_updates(model, inputs, outputs, batch_size, epochs): w, b = get_parameters(model) #model.train_on_batch(inputs, outputs) model.fit(inputs, outputs, batch_size=batch_size, epochs=epochs, verbose=0) w_new, b_new = get_parameters(model) weight_updates = [old - new for old,new in zip(w, w_new)] bias_updates = [old - new for old,new in zip(b, b_new)] return weight_updates, bias_updates # UPDATE THE MODEL'S WEIGHTS AND PARAMETERS WITH AN UPDATE def apply_updates(model, eta, w_new, b_new): w, b = get_parameters(model) new_weights = [theta - eta*delta for theta,delta in zip(w, w_new)] new_biases = [theta - eta*delta for theta,delta in zip(b, b_new)] set_parameters(model, new_weights, new_biases) # FEDERATED AGGREGATION FUNCTION def aggregate(n_layers, n_peers, f, w_updates, b_updates): agg_w = [f([w_updates[j][i] for j in range(n_peers)], axis=0) for i in range(n_layers)] agg_b = [f([b_updates[j][i] for j in range(n_peers)], axis=0) for i in range(n_layers)] return agg_w, agg_b # SOLVE NANS def nans_to_zero(W, B): W0 = [np.nan_to_num(w, nan=0.0, posinf=0.0, neginf=0.0) for w in W] B0 = [np.nan_to_num(b, nan=0.0, posinf=0.0, neginf=0.0) for b in B] return W0, B0 def build_forest(X,y): clf=RandomForestClassifier(n_estimators=1000, max_depth=7, random_state=0, verbose = 1) clf.fit(X,y) return clf # COMPUTE EUCLIDEAN DISTANCE OF WEIGHTS def dist_weights(w_a, w_b): wf_a = flatten_weights(w_a) wf_b = flatten_weights(w_b) return euclidean(wf_a, wf_b) # TRANSFORM ALL WEIGHT TENSORS TO 1D ARRAY def flatten_weights(w_in): h = w_in[0].reshape(-1) for w in w_in[1:]: h = np.append(h, w.reshape(-1)) return h
In [9]:
# scan the forest for trees maches the wrong predictions of the black-box def scan_wrong(forest_predictions, FL_predict1, forest , y_test_local, X_test_local): sum_feature_improtance= 0 overal_wrong_feature_importance = 0 counter = 0 second_counter = 0 never_seen = 0 avr_wrong_importance = 0 FL_predict1 = np.argmax(FL_predict1, axis=1) forest_predictions = np.argmax(forest_predictions, axis=1) y_test_local = np.argmax(y_test_local, axis=1) for i in range (len(FL_predict1)): i_tree = 0 # if the black-box got a wrong prediction if (FL_predict1[i] != y_test_local[i]): # getting the prediction of the trees one by one for tree_in_forest in forest.estimators_: sample = X_test_local[i].reshape(1, -1) temp = forest.estimators_[i_tree].predict(sample) temp = np.argmax(temp, axis=1) i_tree = i_tree + 1 # if the prediction of the tree maches the predictions of the black-box if(FL_predict1[i] == temp): # getting the features importances sum_feature_improtance = sum_feature_improtance + tree_in_forest.feature_importances_ counter = counter + 1 # if we have trees maches the black-box predictions if(counter>0): ave_feature_importence = sum_feature_improtance/counter overal_wrong_feature_importance = ave_feature_importence + overal_wrong_feature_importance second_counter = second_counter + 1 counter = 0 sum_feature_improtance = 0 # if there is no trees maches the black-box predictions else: if(FL_predict1[i] != y_test_local[i]): never_seen = never_seen +1 # getting the average features importances for all the samples that had wrong predictions. if(second_counter>0): avr_wrong_importance = overal_wrong_feature_importance / second_counter return forest.feature_importances_
In [10]:
trainable_layers(model)
Out[10]:
[0, 1, 2, 3]
In [11]:
get_parameters(model)
Out[11]:
([array([[ 1.39432400e-01, 8.84631574e-02, -4.47415888e-01,
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1.04925461e-01, 1.02081522e-02, 1.51008025e-01,
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1.07909396e-01, 5.35671674e-02, -2.25077912e-01,
1.48774251e-01],
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In [12]:
get_updates(model, X_train, y_train, 32, 2)
Out[12]:
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In [13]:
W = get_parameters(model)[0] B = get_parameters(model)[1]
In [14]:
# BASELINE SCENARIO #buid the model as base line for the shards (sequential) # Number of peers #accordin to what we need ss = int(len(X_train)/n_peers) inputs_in = X_train[0*ss:0*ss+ss] outputs_in = y_train[0*ss:0*ss+ss] def build_model(X_t, y_t): model = Sequential() model.add(Dense(70, input_dim=Features_number, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(50, activation='relu')) model.add(Dense(2, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy']) model.fit(X_t, y_t, batch_size=32, epochs=250, verbose=1, validation_data=((X_test, y_test))) return model
In [15]:
display(model.summary())
Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 70) 910 _________________________________________________________________ dense_2 (Dense) (None, 50) 3550 _________________________________________________________________ dense_3 (Dense) (None, 50) 2550 _________________________________________________________________ dense_4 (Dense) (None, 2) 102 ================================================================= Total params: 7,112 Trainable params: 7,112 Non-trainable params: 0 _________________________________________________________________
None
In [16]:
# predict probabilities for test set yhat_probs = model.predict(X_test, verbose=0) # predict crisp classes for test set yhat_classes = model.predict_classes(X_test, verbose=0)
In [17]:
# accuracy: (tp + tn) / (p + n) accuracy = accuracy_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('Accuracy: %f' % accuracy) # precision tp / (tp + fp) precision = precision_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('Precision: %f' % precision) # recall: tp / (tp + fn) recall = recall_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('Recall: %f' % recall) # f1: 2 tp / (2 tp + fp + fn) f1 = f1_score(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) print('F1 score: %f' % f1)
Accuracy: 0.836071 Precision: 0.791075 Recall: 0.483871 F1 score: 0.600462
In [18]:
# confusion matrix mat = confusion_matrix(np.argmax(y_test, axis=1), np.argmax(model.predict(X_test), axis=1)) display(mat) plt.matshow(mat); plt.colorbar() plt.show()
array([[2257, 103],
[ 416, 390]], dtype=int64)
In [19]:
# the dectinary FI_dic1= {0:[],1:[],2:[],3:[],4:[],5:[],6:[],7:[],8:[],9:[]}
In [ ]:
# select aa random peer to be the scanner peer peers_selected=random.sample(range(n_peers), 1) scaner = peers_selected[0] # Percentage and number of peers participating at each global training epoch percentage_participants = 1.0 n_participants = int(n_peers * percentage_participants) # Number of global training epochs n_rounds = 10 start_attack_round = 4 end_attack_round = 7 # Number of local training epochs per global training epoch n_local_rounds = 5 # Local batch size local_batch_size = 32 # Local learning rate local_lr = 0.001 # Global learning rate or 'gain' model_substitution_rate = 1.0 # Attack detection / prevention mechanism = {None, 'distance', 'median', 'accuracy', 'krum'} discard_outliers = None # Used in 'dist' attack detection, defines how far the outliers are (1.5 is a typical value) tau = 1.5 # Used in 'accuracy' attack detection, defines the error margin for the accuracy improvement sensitivity = 0.05 # Used in 'krum' attack detection, defines how many byzantine attackers we want to defend against tolerance=4 # Prevent suspicious peers from participating again, only valid for 'dist' and 'accuracy' ban_malicious = False # Clear nans and infinites in model updates clear_nans = True number_for_threshold1 = numpy.empty(20, dtype=float) number_for_threshold2 = numpy.empty(20, dtype=float) for r in range(len(number_for_threshold1)): number_for_threshold1[r] = 0 number_for_threshold2[r] = 0 ######################## # ATTACK CONFIGURATION # ######################## # Percentage of malicious peers r_malicious_peers = 0.0 # Number of malicious peers (absolute or relative to total number of peers) n_malicious_peers = int(n_peers * r_malicious_peers) #n_malicious_peers = 1 # Malicious peers malicious_peer = range(n_malicious_peers) # Target for coalitions common_attack_target = [4,7] # Target class of the attack, per each malicious peer malicious_targets = dict([(p, t) for p,t in zip(malicious_peer, [common_attack_target]*n_malicious_peers)]) # Boosting parameter per each malicious peer common_malicious_boost = 12 malicious_boost = dict([(p, b) for p,b in zip(malicious_peer, [common_malicious_boost]*n_malicious_peers)]) ########### # METRICS # ########### metrics = {'accuracy': [], 'atk_effectivity': [], 'update_distances': [], 'outliers_detected': [], 'acc_no_target': []} #################################### # MODEL AND NETWORK INITIALIZATION # #################################### inputs = X_train[0*ss:0*ss+ss] outputs = y_train[0*ss:0*ss+ss] global_model = build_model(inputs,outputs) n_layers = len(trainable_layers(global_model)) print('Initializing network.') sleep(1) network = [] for i in tqdm(range(n_peers)): ss = int(len(X_train)/n_peers) inputs = X_train[i*ss:i*ss+ss] outputs = y_train[i*ss:i*ss+ss] # network.append(build_model(inputs, outputs)) network.append(global_model) banned_peers = set() ################## # BEGIN TRAINING # ################## for t in range(n_rounds): print(f'Round {t+1}.') sleep(1) ## SERVER SIDE ################################################################# # Fetch global model parameters global_weights, global_biases = get_parameters(global_model) if clear_nans: global_weights, global_biases = nans_to_zero(global_weights, global_biases) # Initialize peer update lists network_weight_updates = [] network_bias_updates = [] # Selection of participant peers in this global training epoch if ban_malicious: good_peers = list([p for i,p in enumerate(network) if i not in banned_peers]) n_participants = n_participants if n_participants <= len(good_peers) else int(len(good_peers) * percentage_participants) participants = random.sample(list(enumerate(good_peers)), n_participants) else: participants = random.sample(list(enumerate(network)),n_participants) ################################################################################ ## CLIENT SIDE ################################################################# for i, local_model in tqdm(participants): # Update local model with global parameters set_parameters(local_model, global_weights, global_biases) # Initialization of user data ss = int(len(X_train)/n_peers) inputs = X_train[i*ss:i*ss+ss] outputs = y_train[i*ss:i*ss+ss] # the scanner peer side if(i == scaner): X_train_local, X_test_local, y_train_local, y_test_local = train_test_split(inputs,outputs, test_size=0.7, random_state=rs) inputs = X_train_local outputs = y_train_local if(t == 0): forest = build_forest(X_train_local,y_train_local) forest_predictions = forest.predict(X_test_local) acc_forest = np.mean([t==p for t,p in zip(y_test_local, forest_predictions)]) FL_predict1 = global_model.predict(X_test_local) imp = scan_wrong(forest_predictions, FL_predict1, forest , y_test_local, X_test_local) FI_dic1[t] = imp # Benign peer # Train local model local_weight_updates, local_bias_updates = get_updates(local_model, inputs, outputs, local_batch_size, n_local_rounds) if clear_nans: local_weight_updates, local_bias_updates = nans_to_zero(local_weight_updates, local_bias_updates) network_weight_updates.append(local_weight_updates) network_bias_updates.append(local_bias_updates) ## END OF CLIENT SIDE ########################################################## ###################################### # SERVER SIDE AGGREGATION MECHANISMS # ###################################### # Aggregate client updates aggregated_weights, aggregated_biases = aggregate(n_layers, n_participants, np.mean, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) # Apply updates to global model apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) # Proceed as in first case aggregated_weights, aggregated_biases = aggregate(n_layers, n_participants, np.mean, network_weight_updates, network_bias_updates) if clear_nans: aggregated_weights, aggregated_biases = nans_to_zero(aggregated_weights, aggregated_biases) apply_updates(global_model, model_substitution_rate, aggregated_weights, aggregated_biases) ################### # COMPUTE METRICS # ################### # Global model accuracy score = global_model.evaluate(X_test, y_test, verbose=0) print(f'Global model loss: {score[0]}; global model accuracy: {score[1]}') metrics['accuracy'].append(score[1]) # Accuracy without the target score = global_model.evaluate(X_test, y_test, verbose=0) metrics['acc_no_target'].append(score[1]) # Distance of individual updates to the final aggregation metrics['update_distances'].append([dist_weights(aggregated_weights, w_i) for w_i in network_weight_updates])
Train on 420 samples, validate on 3166 samples Epoch 1/250 420/420 [==============================] - ETA: 1s - loss: 0.8020 - accuracy: 0.15 - 0s 475us/step - loss: 0.6159 - accuracy: 0.6571 - val_loss: 0.5505 - val_accuracy: 0.7454 Epoch 2/250 420/420 [==============================] - ETA: 0s - loss: 0.4634 - accuracy: 0.81 - 0s 221us/step - loss: 0.5195 - accuracy: 0.7667 - val_loss: 0.5165 - val_accuracy: 0.7454 Epoch 3/250 420/420 [==============================] - ETA: 0s - loss: 0.4718 - accuracy: 0.78 - 0s 197us/step - loss: 0.4819 - accuracy: 0.7667 - val_loss: 0.4850 - val_accuracy: 0.7454 Epoch 4/250 420/420 [==============================] - ETA: 0s - loss: 0.4996 - accuracy: 0.78 - 0s 202us/step - loss: 0.4604 - accuracy: 0.7667 - val_loss: 0.4648 - val_accuracy: 0.7454 Epoch 5/250 420/420 [==============================] - ETA: 0s - loss: 0.4819 - accuracy: 0.81 - 0s 216us/step - loss: 0.4435 - accuracy: 0.7690 - val_loss: 0.4511 - val_accuracy: 0.7527 Epoch 6/250 420/420 [==============================] - ETA: 0s - loss: 0.5019 - accuracy: 0.71 - 0s 180us/step - loss: 0.4364 - accuracy: 0.7667 - val_loss: 0.4513 - val_accuracy: 0.7489 Epoch 7/250 420/420 [==============================] - ETA: 0s - loss: 0.4597 - accuracy: 0.71 - 0s 164us/step - loss: 0.4270 - accuracy: 0.7857 - val_loss: 0.4398 - val_accuracy: 0.7748 Epoch 8/250 420/420 [==============================] - ETA: 0s - loss: 0.4168 - accuracy: 0.75 - 0s 178us/step - loss: 0.4183 - accuracy: 0.8048 - val_loss: 0.4361 - val_accuracy: 0.7761 Epoch 9/250 420/420 [==============================] - ETA: 0s - loss: 0.3737 - accuracy: 0.75 - 0s 199us/step - loss: 0.4082 - accuracy: 0.8167 - val_loss: 0.4300 - val_accuracy: 0.7738 Epoch 10/250 420/420 [==============================] - ETA: 0s - loss: 0.4122 - accuracy: 0.84 - 0s 180us/step - loss: 0.4041 - accuracy: 0.8024 - val_loss: 0.4348 - val_accuracy: 0.7798 Epoch 11/250 420/420 [==============================] - ETA: 0s - loss: 0.5593 - accuracy: 0.62 - 0s 185us/step - loss: 0.3933 - accuracy: 0.8167 - val_loss: 0.4230 - val_accuracy: 0.7817 Epoch 12/250 420/420 [==============================] - ETA: 0s - loss: 0.3269 - accuracy: 0.84 - 0s 190us/step - loss: 0.3849 - accuracy: 0.8167 - val_loss: 0.4186 - val_accuracy: 0.7890 Epoch 13/250 420/420 [==============================] - ETA: 0s - loss: 0.4152 - accuracy: 0.87 - 0s 197us/step - loss: 0.3739 - accuracy: 0.8190 - val_loss: 0.4109 - val_accuracy: 0.7922 Epoch 14/250 420/420 [==============================] - ETA: 0s - loss: 0.3175 - accuracy: 0.87 - 0s 212us/step - loss: 0.3681 - accuracy: 0.8357 - val_loss: 0.4049 - val_accuracy: 0.7953 Epoch 15/250 420/420 [==============================] - ETA: 0s - loss: 0.2642 - accuracy: 0.87 - 0s 254us/step - loss: 0.3643 - accuracy: 0.8190 - val_loss: 0.3993 - val_accuracy: 0.8061 Epoch 16/250 420/420 [==============================] - ETA: 0s - loss: 0.3769 - accuracy: 0.84 - 0s 230us/step - loss: 0.3681 - accuracy: 0.8286 - val_loss: 0.3994 - val_accuracy: 0.8016 Epoch 17/250 420/420 [==============================] - ETA: 0s - loss: 0.4769 - accuracy: 0.84 - 0s 214us/step - loss: 0.3530 - accuracy: 0.8381 - val_loss: 0.3918 - val_accuracy: 0.8092 Epoch 18/250 420/420 [==============================] - ETA: 0s - loss: 0.3678 - accuracy: 0.87 - 0s 202us/step - loss: 0.3377 - accuracy: 0.8310 - val_loss: 0.3871 - val_accuracy: 0.8130 Epoch 19/250 420/420 [==============================] - ETA: 0s - loss: 0.3190 - accuracy: 0.81 - 0s 230us/step - loss: 0.3469 - accuracy: 0.8310 - val_loss: 0.4820 - val_accuracy: 0.7738 Epoch 20/250 420/420 [==============================] - ETA: 0s - loss: 0.3204 - accuracy: 0.84 - 0s 199us/step - loss: 0.3986 - accuracy: 0.8167 - val_loss: 0.4357 - val_accuracy: 0.7713 Epoch 21/250 420/420 [==============================] - ETA: 0s - loss: 0.4518 - accuracy: 0.75 - 0s 211us/step - loss: 0.3504 - accuracy: 0.8286 - val_loss: 0.4117 - val_accuracy: 0.7979 Epoch 22/250 420/420 [==============================] - ETA: 0s - loss: 0.4504 - accuracy: 0.81 - 0s 197us/step - loss: 0.3356 - accuracy: 0.8357 - val_loss: 0.3779 - val_accuracy: 0.8222 Epoch 23/250 420/420 [==============================] - ETA: 0s - loss: 0.3286 - accuracy: 0.81 - 0s 181us/step - loss: 0.3188 - accuracy: 0.8357 - val_loss: 0.4099 - val_accuracy: 0.7982 Epoch 24/250 420/420 [==============================] - ETA: 0s - loss: 0.5356 - accuracy: 0.81 - 0s 178us/step - loss: 0.3277 - accuracy: 0.8310 - val_loss: 0.3724 - val_accuracy: 0.8247 Epoch 25/250 420/420 [==============================] - ETA: 0s - loss: 0.2666 - accuracy: 0.87 - 0s 192us/step - loss: 0.3120 - accuracy: 0.8476 - val_loss: 0.3793 - val_accuracy: 0.8181 Epoch 26/250 420/420 [==============================] - ETA: 0s - loss: 0.1937 - accuracy: 0.90 - 0s 191us/step - loss: 0.3137 - accuracy: 0.8595 - val_loss: 0.3741 - val_accuracy: 0.8272 Epoch 27/250 420/420 [==============================] - ETA: 0s - loss: 0.3485 - accuracy: 0.81 - 0s 228us/step - loss: 0.3016 - accuracy: 0.8571 - val_loss: 0.3725 - val_accuracy: 0.8241 Epoch 28/250 420/420 [==============================] - ETA: 0s - loss: 0.1122 - accuracy: 1.00 - 0s 261us/step - loss: 0.2970 - accuracy: 0.8548 - val_loss: 0.3731 - val_accuracy: 0.8222 Epoch 29/250 420/420 [==============================] - ETA: 0s - loss: 0.2215 - accuracy: 0.90 - 0s 197us/step - loss: 0.3014 - accuracy: 0.8476 - val_loss: 0.3781 - val_accuracy: 0.8174 Epoch 30/250 420/420 [==============================] - ETA: 0s - loss: 0.2079 - accuracy: 0.84 - 0s 204us/step - loss: 0.2942 - accuracy: 0.8548 - val_loss: 0.3813 - val_accuracy: 0.8181 Epoch 31/250 420/420 [==============================] - ETA: 0s - loss: 0.3559 - accuracy: 0.75 - 0s 225us/step - loss: 0.2938 - accuracy: 0.8429 - val_loss: 0.3857 - val_accuracy: 0.8105 Epoch 32/250 420/420 [==============================] - ETA: 0s - loss: 0.4537 - accuracy: 0.78 - 0s 239us/step - loss: 0.3120 - accuracy: 0.8500 - val_loss: 0.3785 - val_accuracy: 0.8184 Epoch 33/250 420/420 [==============================] - ETA: 0s - loss: 0.1957 - accuracy: 0.90 - 0s 229us/step - loss: 0.3323 - accuracy: 0.8500 - val_loss: 0.4259 - val_accuracy: 0.7997 Epoch 34/250 420/420 [==============================] - ETA: 0s - loss: 0.2729 - accuracy: 0.87 - 0s 212us/step - loss: 0.2830 - accuracy: 0.8738 - val_loss: 0.3710 - val_accuracy: 0.8282 Epoch 35/250 420/420 [==============================] - ETA: 0s - loss: 0.4034 - accuracy: 0.78 - 0s 245us/step - loss: 0.2836 - accuracy: 0.8643 - val_loss: 0.3741 - val_accuracy: 0.8272 Epoch 36/250 420/420 [==============================] - ETA: 0s - loss: 0.2914 - accuracy: 0.87 - 0s 219us/step - loss: 0.2785 - accuracy: 0.8667 - val_loss: 0.3696 - val_accuracy: 0.8256 Epoch 37/250 420/420 [==============================] - ETA: 0s - loss: 0.2081 - accuracy: 0.90 - 0s 252us/step - loss: 0.2737 - accuracy: 0.8690 - val_loss: 0.3721 - val_accuracy: 0.8244 Epoch 38/250 420/420 [==============================] - ETA: 0s - loss: 0.2847 - accuracy: 0.90 - 0s 185us/step - loss: 0.2932 - accuracy: 0.8762 - val_loss: 0.3984 - val_accuracy: 0.8149 Epoch 39/250 420/420 [==============================] - ETA: 0s - loss: 0.2068 - accuracy: 0.96 - 0s 275us/step - loss: 0.2746 - accuracy: 0.8786 - val_loss: 0.3747 - val_accuracy: 0.8260 Epoch 40/250 420/420 [==============================] - ETA: 0s - loss: 0.2561 - accuracy: 0.87 - 0s 235us/step - loss: 0.2991 - accuracy: 0.8524 - val_loss: 0.3819 - val_accuracy: 0.8253 Epoch 41/250 420/420 [==============================] - ETA: 0s - loss: 0.2118 - accuracy: 0.90 - 0s 188us/step - loss: 0.2920 - accuracy: 0.8429 - val_loss: 0.3901 - val_accuracy: 0.8143 Epoch 42/250 420/420 [==============================] - ETA: 0s - loss: 0.1978 - accuracy: 0.90 - 0s 230us/step - loss: 0.2979 - accuracy: 0.8667 - val_loss: 0.4308 - val_accuracy: 0.8080 Epoch 43/250 420/420 [==============================] - ETA: 0s - loss: 0.3652 - accuracy: 0.75 - 0s 233us/step - loss: 0.2874 - accuracy: 0.8571 - val_loss: 0.3759 - val_accuracy: 0.8266 Epoch 44/250
420/420 [==============================] - ETA: 0s - loss: 0.2414 - accuracy: 0.90 - 0s 211us/step - loss: 0.2671 - accuracy: 0.8643 - val_loss: 0.3861 - val_accuracy: 0.8152 Epoch 45/250 420/420 [==============================] - ETA: 0s - loss: 0.1696 - accuracy: 0.96 - 0s 211us/step - loss: 0.2889 - accuracy: 0.8738 - val_loss: 0.3877 - val_accuracy: 0.8234 Epoch 46/250 420/420 [==============================] - ETA: 0s - loss: 0.2738 - accuracy: 0.84 - 0s 220us/step - loss: 0.2635 - accuracy: 0.8738 - val_loss: 0.3914 - val_accuracy: 0.8222 Epoch 47/250 32/420 [=>............................] - ETA: 0s - loss: 0.2366 - accuracy: 0.9062
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# sort the feature according to the last epoch and print it with importances sort_index = np.argsort(FI_dic1[9]) for x in sort_index: print(names[x], ', ', FI_dic1[9][x])
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