From 44804a2f8261ad19beafa385fb3ebcb1fab1aba1 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Tue, 20 Sep 2016 17:33:53 -0700 Subject: [PATCH 01/27] Specify evaluation type in YAML file --- attack.yaml | 6 ++++++ fpsd/classify.py | 1 + 2 files changed, 7 insertions(+) create mode 100644 attack.yaml create mode 100644 fpsd/classify.py diff --git a/attack.yaml b/attack.yaml new file mode 100644 index 00000000..d0c01818 --- /dev/null +++ b/attack.yaml @@ -0,0 +1,6 @@ +################# +# Train/Test # +################# + +evaluation: 'closed' # or 'open' + diff --git a/fpsd/classify.py b/fpsd/classify.py new file mode 100644 index 00000000..6b8389dd --- /dev/null +++ b/fpsd/classify.py @@ -0,0 +1 @@ +from sklearn import cross_validation, ensemble \ No newline at end of file From 8dec95408d610fd5f1f212256e01d8daf8ccac48 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 21 Sep 2016 12:10:07 -0700 Subject: [PATCH 02/27] Refactor engine creation and add initial DatasetLoader class --- fpsd/database.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/fpsd/database.py b/fpsd/database.py index 21a44aba..d25aa024 100644 --- a/fpsd/database.py +++ b/fpsd/database.py @@ -11,6 +11,7 @@ from utils import get_config, get_lookback, get_timestamp, panic + class Database: """A base class for database objects providing an engine and context management for safe transactions. @@ -178,3 +179,9 @@ def add_trace(self, trace, exampleid): with self.safe_session() as session: session.bulk_save_objects(cells) return None + + +class DatasetLoader(Database): + """Load train/test sets""" + def __init__(self): + super().__init__(**kwargs) From b221b3938ce43fc32323f22e14f02da54585b239 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 21 Sep 2016 13:49:29 -0700 Subject: [PATCH 03/27] Add generic function for getting feature importances of trained model --- fpsd/evaluation.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 fpsd/evaluation.py diff --git a/fpsd/evaluation.py b/fpsd/evaluation.py new file mode 100644 index 00000000..02240b81 --- /dev/null +++ b/fpsd/evaluation.py @@ -0,0 +1,15 @@ +def get_feature_importances(model): + + try: + return model.feature_importances_ + except: + pass + try: + # Must be 1D for feature importance plot + if len(model.coef_) <= 1: + return model.coef_[0] + else: + return model.coef_ + except: + pass + return None From c2fb467626d586bfb5ddef60fd4d1447515ad161 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 21 Sep 2016 13:50:25 -0700 Subject: [PATCH 04/27] Add function for plotting feature importances --- fpsd/evaluation.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/fpsd/evaluation.py b/fpsd/evaluation.py index 02240b81..daa790f1 100644 --- a/fpsd/evaluation.py +++ b/fpsd/evaluation.py @@ -13,3 +13,28 @@ def get_feature_importances(model): except: pass return None + + +def plot_feature_importances(feature_names, feature_importances, N=30): + importances = list(zip(feature_names, list(feature_importances))) + importances = pd.DataFrame(importances, columns=["Feature", "Importance"]) + importances = importances.set_index("Feature") + + # Sort by the absolute value of the importance of the feature + importances["sort"] = abs(importances["Importance"]) + importances = importances.sort(columns="sort", ascending=False).drop("sort", axis=1) + importances = importances[0:N] + + # Show the most important positive feature at the top of the graph + importances = importances.sort(columns="Importance", ascending=True) + + with plt.style.context(('ggplot')): + fig, ax = plt.subplots(figsize=(16,12)) + ax.tick_params(labelsize=16) + importances.plot(kind="barh", legend=False, ax=ax) + ax.set_frame_on(False) + ax.set_xlabel("Relative importance", fontsize=20) + ax.set_ylabel("Feature name", fontsize=20) + plt.tight_layout() + plt.title("Most important features for attack", fontsize=20).set_position([.5, 0.99]) + return fig \ No newline at end of file From 260ba7cb52c5fb7be6486a42975843b4cf7cc2ba Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 21 Sep 2016 13:50:54 -0700 Subject: [PATCH 05/27] Add function for ROC plot --- fpsd/evaluation.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/fpsd/evaluation.py b/fpsd/evaluation.py index daa790f1..6599a344 100644 --- a/fpsd/evaluation.py +++ b/fpsd/evaluation.py @@ -37,4 +37,22 @@ def plot_feature_importances(feature_names, feature_importances, N=30): ax.set_ylabel("Feature name", fontsize=20) plt.tight_layout() plt.title("Most important features for attack", fontsize=20).set_position([.5, 0.99]) + return fig + + +def plot_ROC(test_labels, test_predictions): + fpr, tpr, thresholds = metrics.roc_curve( + test_labels, test_predictions, pos_label=1) + auc = "%.2f" % metrics.auc(fpr, tpr) + title = 'ROC Curve, AUC = '+str(auc) + with plt.style.context(('ggplot')): + fig, ax = plt.subplots() + ax.plot(fpr, tpr, "#000099", label='ROC curve') + ax.plot([0, 1], [0, 1], 'k--', label='Baseline') + plt.xlim([0.0, 1.0]) + plt.ylim([0.0, 1.05]) + plt.xlabel('False Positive Rate') + plt.ylabel('True Positive Rate') + plt.legend(loc='lower right') + plt.title(title) return fig \ No newline at end of file From d4e306bcd373f4ed350baa23d06dc2cda86ec4ea Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 21 Sep 2016 15:09:08 -0700 Subject: [PATCH 06/27] Add machine learning classifier and CV code, add Experiment() classifier wrapper --- fpsd/attack.py | 79 +++++++++ attack.yaml => fpsd/attack.yaml | 0 fpsd/classify.py | 278 +++++++++++++++++++++++++++++++- fpsd/evaluation.py | 137 +++++++++++++++- 4 files changed, 492 insertions(+), 2 deletions(-) create mode 100644 fpsd/attack.py rename attack.yaml => fpsd/attack.yaml (100%) diff --git a/fpsd/attack.py b/fpsd/attack.py new file mode 100644 index 00000000..cafce1a4 --- /dev/null +++ b/fpsd/attack.py @@ -0,0 +1,79 @@ +import argparse +import datetime +from itertools import product +import pdb +import pickle +import yaml + +import classify, database + + +def run(options): + """Takes an attack file, gets the features, and runs all experiments + and saves the output in the database. + + Args: + options [dict]: attack setup file + """ + + with open(options, 'r') as f: + options = yaml.load(f) + + db = database.DatasetLoader() + + if options["world"]["type"] == "closed": + df = db.load_closed_world() + elif options["world"]["type"] == "open": + df = db.load_open_world() + + df = classify.interpolate(df) + x = df.drop(['exampleid', 'is_sd'], axis=1).values + y = df['is_sd'].astype(int).values + + experiments = generate_experiments(options) + for experiment in experiments: + experiment.train_eval_all_folds(x, y) + + +def generate_experiments(options): + """Takes an attack file and generates all the experiments that + should be run + + Args: + options [dict]: attack setup file + + Returns: + all_experiments [list]: list of Experiment objects + """ + + all_experiments = [] + + for model in options["models"]: + model_hyperparameters = options["parameters"][model] + + parameter_names = sorted(model_hyperparameters) + parameter_values = [model_hyperparameters[p] for p in parameter_names] + all_params = product(*parameter_values) + + for param in all_params: + parameters = {name: value for name, value + in zip(parameter_names, param)} + + timestamp = datetime.datetime.now().isoformat() + + all_experiments.append(classify.Experiment( + model_timestamp=timestamp, + world=options["world"], + model_type=model, + hyperparameters=parameters, + feature_scaling=options["feature_scaling"])) + return all_experiments + + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument("-a", "--setup-yml", dest="attack_setup", type=str, default="attack.yaml", + help="point to attack setup file") + args = parser.parse_args() + + run(args.attack_setup) diff --git a/attack.yaml b/fpsd/attack.yaml similarity index 100% rename from attack.yaml rename to fpsd/attack.yaml diff --git a/fpsd/classify.py b/fpsd/classify.py index 6b8389dd..9b54c6da 100644 --- a/fpsd/classify.py +++ b/fpsd/classify.py @@ -1 +1,277 @@ -from sklearn import cross_validation, ensemble \ No newline at end of file +import datetime +import matplotlib.pyplot as plt +import numpy as np +import pickle +from scipy import interp +from sklearn import (cross_validation, ensemble, metrics, svm, tree, + linear_model, neighbors, naive_bayes, + preprocessing) + + +import evaluation, database + + +def interpolate(df): + """Handle missing values in our data. This is mostly a + placeholder for when we have a better way to handle this. + + Args: + df [pandas DataFrame]: input data with NaNs + + Returns: + df [pandas DataFrame]: output data with NaNs filled to 0 + """ + + return df.fillna(0) + + +class Experiment(object): + def __init__(self, model_timestamp, world, model_type, + hyperparameters, + feature_scaling=True, n_cores=2, k=10): + """ + Args: + model [string]: machine learning algorithm to be used + parameters [dict]: hyperparameter set to be used for the + machine learning algorithm + k [int]: number of k-folds + world [dict]: world type (open- or closed- world) + and parameters if necessary + """ + + self.model_timestamp = model_timestamp + self.hyperparameters = hyperparameters + self.model_type = model_type + self.world_type = world["type"] + self.frac_obs = world["observed_fraction"] + self.n_cores = n_cores + self.k = k + self.feature_scaling = feature_scaling + self.db = database.ModelStorage() + + def get_dict(self): + """Get config options as a dict""" + + return {"hyperparameters": self.hyperparameters, + "model_type": self.model_type, + "world_type": self.world_type, + "observed_world_fraction": self.frac_obs, + "numfolds": self.k, + "train_class_balance": 'DEFAULT', + "base_rate": 'DEFAULT'} + + def train_single_fold(self, train_x, train_y): + """Trains a model and saves it as self.trained_model + + Args: + train_x [ndarray]: features in training set (no id column, no target) + train_y [ndarray]: target variable in training set (no id column) + """ + + print("Training {} classifier with {}".format(self.model_type, + self.hyperparameters)) + + modelobj = self._get_model_object(self.model_type, + self.hyperparameters, + self.n_cores) + trained_model = modelobj.fit(train_x, train_y) + return trained_model + + def score(self, test_x, trained_model): + """Generates continuous risk scores for a testing set. + + Args: + test_x [ndarray]: testing features + trained_model [sklearn object]: trained classifier object + + Returns: + result_y [ndarray]: predictions on test set + """ + + result_y = trained_model.predict_proba(test_x) + score_positive_class = result_y[:, 1] + return score_positive_class + + def train_eval_all_folds(self, x_val, y_val): + """Trains and evaluates models over all folds. + + Args: + timestamp [string]: timestamp of the time the model is run, + used as model identifier + x_val [ndarray]: feature matrix + y_val [ndarray]: label vector + + Returns: + auc [float]: area under the ROC curve + mean_fpr [list of floats]: false positive rate averaged over + all kfolds + mean_tpr [list of floats]: true positive rate averaged over + all kfolds + trained_model [sklearn object]: trained object to be pickled + so that it can be used for + scoring + """ + + if self.world_type == "closed": + # Why we use stratified k-fold here: + # http://stats.stackexchange.com/questions/49540/understanding-stratified-cross-validation + cv = cross_validation.StratifiedKFold(y_val, n_folds=self.k, + shuffle=True) + elif self.world_type == "open": + pass # TODO + + fpr_arr, tpr_arr, metrics_all_folds = [], [], [] + for i, (train, test) in enumerate(cv): + + fold_timestamp = datetime.datetime.now().isoformat() + y_train, y_test = y_val[train], y_val[test] + + if self.feature_scaling: + scaler = preprocessing.StandardScaler().fit(x_val[train]) + x_train = scaler.transform(x_val[train]) + x_test = scaler.transform(x_val[test]) + else: + x_train, x_test = x_val[train], x_val[test] + + trained_model = self.train_single_fold(x_train, y_train) + pred_probs = self.score(x_test, trained_model) + + filename_kfold = '{}_{}_undefended_frontpage_{}_model_{}_fold_{}_world.pkl'.format( + fold_timestamp, self.model_timestamp, self.model_type, i, self.world_type) + fold_to_save = {'trained_object': trained_model, + 'y_true': y_test, 'y_predicted': pred_probs} + self.pickle_results(filename_kfold, fold_to_save) + + # Metrics computation + # Compute ROC curve and area under the ROC curve + eval_metrics = evaluation.get_metrics(y_test, pred_probs) + metrics_all_folds.append(eval_metrics) + fpr_arr.append(eval_metrics['fpr']) + tpr_arr.append(eval_metrics['tpr']) + + # Save results of metrics in database + self.db.save_fold_of_model(eval_metrics, self.model_timestamp, fold_timestamp) + + auc = evaluation.plot_allkfolds_ROC(self.model_timestamp, cv, + fpr_arr, tpr_arr) + + print("Classifier {} trained! AUC: {}".format(self.model_timestamp, + auc)) + + avg_metrics = evaluation.get_average_metrics(metrics_all_folds) + # Save results of experiment (model evaluation averaged over all + # folds) into the database + self.db.save_full_model(avg_metrics, self.model_timestamp, self.get_dict()) + + def pickle_results(self, pkl_file, to_save): + with open(pkl_file, 'wb') as f: + pickle.dump(to_save, f, protocol=pickle.HIGHEST_PROTOCOL) + + def _get_model_object(self, model, parameters, n_cores): + """This method takes the requested model type and + hyperparameters and produces the relevant classifier object. + + Returns: + object with the fit() and predict_proba() methods + implemented on it + """ + + if self.model_type == "RandomForest": + return ensemble.RandomForestClassifier( + n_estimators=self.hyperparameters['n_estimators'], + max_features=self.hyperparameters['max_features'], + criterion=self.hyperparameters['criterion'], + max_depth=self.hyperparameters['max_depth'], + min_samples_split=self.hyperparameters['min_samples_split'], + n_jobs=self.n_cores) + + elif self.model_type == "RandomForestBagging": + return ensemble.BaggingClassifier( + ensemble.RandomForestClassifier( + n_estimators=self.hyperparameters['n_estimators'], + max_features=self.hyperparameters['max_features'], + criterion=self.hyperparameters['criterion'], + max_depth=self.hyperparameters['max_depth'], + min_samples_split=self.hyperparameters['min_samples_split'], + n_jobs=self.n_cores), + #Bagging parameters + n_estimators=self.hyperparameters['n_estimators_bag'], + max_samples=self.hyperparameters['max_samples'], + max_features=self.hyperparameters['max_features_bag'], + bootstrap=self.hyperparameters['bootstrap'], + bootstrap_features=self.hyperparameters['bootstrap_features'], + n_jobs=self.n_cores + ) + + elif self.model_type == "RandomForestBoosting": + return ensemble.AdaBoostClassifier( + ensemble.RandomForestClassifier( + n_estimators=self.hyperparameters['n_estimators'], + max_features=self.hyperparameters['max_features'], + criterion=self.hyperparameters['criterion'], + max_depth=self.hyperparameters['max_depth'], + min_samples_split=self.hyperparameters['min_samples_split'], + n_jobs=self.n_cores), + #Boosting parameters + learning_rate=self.hyperparameters['learning_rate'], + algorithm=self.hyperparameters['algorithm'], + n_estimators=self.hyperparameters['n_estimators_boost'] + ) + + elif self.model_type == 'SVM': + return svm.SVC(C=self.hyperparameters['C_reg'], + kernel=self.hyperparameters['kernel'], + probability=True) + + elif self.model_type == 'LogisticRegression': + return linear_model.LogisticRegression( + C=self.hyperparameters['C_reg'], + penalty=self.hyperparameters['penalty']) + + elif self.model_type == 'AdaBoost': + return ensemble.AdaBoostClassifier( + learning_rate=self.hyperparameters['learning_rate'], + algorithm=self.hyperparameters['algorithm'], + n_estimators=self.hyperparameters['n_estimators']) + + elif self.model_type == 'ExtraTrees': + return ensemble.ExtraTreesClassifier( + n_estimators=self.hyperparameters['n_estimators'], + max_features=self.hyperparameters['max_features'], + criterion=self.hyperparameters['criterion'], + max_depth=self.hyperparameters['max_depth'], + min_samples_split=self.hyperparameters['min_samples_split'], + n_jobs=self.n_cores) + + elif self.model_type == 'GradientBoostingClassifier': + return ensemble.GradientBoostingClassifier( + n_estimators=self.hyperparameters['n_estimators'], + learning_rate=self.hyperparameters['learning_rate'], + subsample=self.hyperparameters['subsample'], + max_depth=self.hyperparameters['max_depth']) + + elif self.model_type == 'GaussianNB': + return naive_bayes.GaussianNB() + + elif self.model_type == 'DecisionTreeClassifier': + return tree.DecisionTreeClassifier( + max_features=self.hyperparameters['max_features'], + criterion=self.hyperparameters['criterion'], + max_depth=self.hyperparameters['max_depth'], + min_samples_split=self.hyperparameters['min_samples_split']) + + elif self.model_type == 'SGDClassifier': + return linear_model.SGDClassifier( + loss=self.hyperparameters['loss'], + penalty=self.hyperparameters['penalty'], + n_jobs=self.n_cores) + + elif self.model_type == 'KNeighborsClassifier': + return neighbors.KNeighborsClassifier( + n_neighbors=self.hyperparameters['n_neighbors'], + weights=self.hyperparameters['weights'], + algorithm=self.hyperparameters['algorithm'], + n_jobs=self.n_cores) + + else: + raise ValueError("Unsupported classifier {}".format(self.model_type)) diff --git a/fpsd/evaluation.py b/fpsd/evaluation.py index 6599a344..63cbd9c5 100644 --- a/fpsd/evaluation.py +++ b/fpsd/evaluation.py @@ -1,3 +1,78 @@ +import numpy as np +from scipy import interp +from sklearn import metrics +import pandas as pd +import pdb + +import matplotlib as mpl +mpl.use("Agg") +import matplotlib.pyplot as plt +import seaborn as sns + +THRESHOLDS = [0.01, 0.05, 0.1, 0.5, 1, 5, 10] + +def get_metrics(truth, predicted): + """Compute evaluation metrics for a given fold + + Args: + truth [list]: List of labels + predicted [list]: List of predicted scores + + Returns: + eval_metrics [dict]: dict of metrics to be saved in the db + """ + + eval_metrics = {} + fpr, tpr, thresholds = metrics.roc_curve(truth, predicted) + eval_metrics["fpr"] = fpr + eval_metrics["tpr"] = tpr + eval_metrics["thresholds"] = thresholds + eval_metrics["auc"] = metrics.auc(fpr, tpr) + eval_metrics["precision"] = {} + eval_metrics["recall"] = {} + eval_metrics["f1"] = {} + + for threshold in THRESHOLDS: + precision, recall, f1 = precision_recall_at_x_proportion(truth, + predicted, x_proportion=threshold/100.) + eval_metrics.update({threshold: {'precision': precision, + 'recall': recall, + 'f1': f1}}) + return eval_metrics + + +def get_average_metrics(metrics_list): + """Get algorithm performance over all folds""" + + eval_metrics = {} + + track_metric = 0 + for fold in metrics_list: + track_metric += fold["auc"] + + # Divide by number of folds + track_metric /= len(metrics_list) + eval_metrics.update({"auc": track_metric}) + + for metric in ("fpr", "tpr"): + # the fpr, tpr output from scikit-learn may not have the same + # number of elements in the arrays, set to Null for now + eval_metrics.update({metric: [0, 0]}) + + for threshold in THRESHOLDS: + eval_metrics[threshold] = {} + for metric in ("precision", "recall", "f1"): + track_metric = 0 + for fold in metrics_list: + track_metric += fold[threshold][metric] + + # Divide by number of folds + track_metric /= len(metrics_list) + eval_metrics[threshold].update({metric: track_metric}) + + return eval_metrics + + def get_feature_importances(model): try: @@ -40,6 +115,29 @@ def plot_feature_importances(feature_names, feature_importances, N=30): return fig +def precision_recall_at_x_proportion(test_labels, test_predictions, x_proportion=0.01, + return_cutoff=False): + + cutoff_index = int(len(test_predictions) * x_proportion) + cutoff_index = min(cutoff_index, len(test_predictions) - 1) + + sorted_by_probability = np.sort(test_predictions)[::-1] + cutoff_probability = sorted_by_probability[cutoff_index] + + test_predictions_binary = [1 if x > cutoff_probability else 0 for x in test_predictions] + + precision, recall, f1, _ = metrics.precision_recall_fscore_support( + test_labels, test_predictions_binary) + + # Only interested in metrics for label 1 + precision, recall, f1 = precision[1], recall[1], f1[1] + + if return_cutoff: + return precision, recall, f1, cutoff_probability + else: + return precision, recall, f1 + + def plot_ROC(test_labels, test_predictions): fpr, tpr, thresholds = metrics.roc_curve( test_labels, test_predictions, pos_label=1) @@ -55,4 +153,41 @@ def plot_ROC(test_labels, test_predictions): plt.ylabel('True Positive Rate') plt.legend(loc='lower right') plt.title(title) - return fig \ No newline at end of file + return fig + + +def plot_allkfolds_ROC(timestamp, cv, fpr_arr, tpr_arr): + + sns.set(style="white", palette="muted", color_codes=True) + + mean_tpr = 0.0 + mean_fpr = 0.0 + all_roc_auc = [] + bins_roc = np.linspace(0, 1, 300) + with plt.style.context(('seaborn-muted')): + fig, ax = plt.subplots(figsize=(10, 8)) + for i, (train, test) in enumerate(cv): + mean_tpr += interp(bins_roc, fpr_arr[i], tpr_arr[i]) + mean_tpr[0] = 0.0 + mean_fpr += interp(bins_roc, fpr_arr[i], tpr_arr[i]) + mean_fpr[0] = 0.0 + roc_auc = metrics.auc(fpr_arr[i], tpr_arr[i]) + all_roc_auc.append(roc_auc) + ax.plot(fpr_arr[i], tpr_arr[i], lw=1, label='KFold %d (AUC = %0.2f)' % (i, roc_auc)) + ax.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Random') + + mean_tpr /= len(cv) + mean_tpr[-1] = 1.0 + mean_auc = np.mean(all_roc_auc) + ax.plot(bins_roc, mean_tpr, 'k--', + label='Mean ROC (AUC = %0.2f)' % mean_auc, lw=2) + + ax.set_xlim([-0.05, 1.05]) + ax.set_ylim([-0.05, 1.05]) + ax.set_xlabel('False Positive Rate') + ax.set_ylabel('True Positive Rate') + ax.set_title('Receiver Operating Characteristic') + ax.legend(loc="lower right") + plt.savefig('{}_roc.png'.format(timestamp)) + plt.close('all') + return mean_auc \ No newline at end of file From f47349fafa07a221a39ef7c1fe8d79e78ed9b5a2 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 21 Sep 2016 15:25:04 -0700 Subject: [PATCH 07/27] Add function to load closed world dataset --- fpsd/database.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/fpsd/database.py b/fpsd/database.py index d25aa024..46dc2c5d 100644 --- a/fpsd/database.py +++ b/fpsd/database.py @@ -3,6 +3,7 @@ from contextlib import contextmanager from datetime import datetime as dt import os +import pandas as pd import re from sqlalchemy import create_engine, MetaData from sqlalchemy.engine.url import URL as SQL_connect_URL @@ -185,3 +186,20 @@ class DatasetLoader(Database): """Load train/test sets""" def __init__(self): super().__init__(**kwargs) + + def load_closed_world(self): + """For closed world validation, we can select traces + without consideration of which site they belong to and instead + + Returns: + df [pandas DataFrame]: dataset + """ + + labelled_query = ('select t1.*, t3.is_sd from features.frontpage_features t1 ' + 'inner join raw.frontpage_examples t2 ' + 'on t1.exampleid = t2.exampleid ' + 'inner join raw.hs_history t3 ' + 'on t3.hsid = t2.hsid') + + df = pd.read_sql(labelled_query, self.engine) + return df From 43ea65b632b11d150e5132ad4a6a356592549090 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 21 Sep 2016 22:45:46 -0700 Subject: [PATCH 08/27] Add scikit-learn classifiers and hyperparameters --- fpsd/attack.yaml | 71 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) diff --git a/fpsd/attack.yaml b/fpsd/attack.yaml index d0c01818..b07c3139 100644 --- a/fpsd/attack.yaml +++ b/fpsd/attack.yaml @@ -4,3 +4,74 @@ evaluation: 'closed' # or 'open' + +################# +# Classifiers # +################# + +# All supported models +# model: ['RandomForest', 'RandomForestBagging', 'RandomForestBoosting', 'ExtraTrees', +# 'AdaBoost', 'LogisticRegression', 'SVM', 'GradientBoostingClassifier', +# 'DecisionTreeClassifier', 'SGDClassifier', 'KNeighborsClassifier'] + +models: ['RandomForest'] +parameters: + RandomForest: + n_estimators: [10, 25, 50] # 100, 1000, 10000 + max_depth: [5, 10, 20] # 50, 100 + max_features: ['sqrt'] # ['log2', 2, 4, 8, 16, "auto"] + criterion: ['gini', 'entropy'] + min_samples_split: [2, 5, 10] + RandomForestBagging: + n_estimators: [10] # [25, 50, 100, 1000, 10000] + max_depth: [5] # [10, 20, 50, 100] + max_features: ['sqrt'] # ['log2', 2, 4, 8, 16, "auto"] + criterion: ['gini'] # ['entropy'] + min_samples_split: [2] # [5, 10] + max_samples: [0.5] # [1.0] + bootstrap: [True] + bootstrap_features: [False] # [True] + n_estimators_bag: [10] # [25, 50, 100, 1000, 10000] + max_features_bag: [2] # [4, 8, 16] + RandomForestBoosting: + n_estimators: [100] # [25, 50, 100, 1000, 10000] + max_depth: [20] # [10, 20, 50, 100] + max_features: [2] # ['sqrt', 'log2', 2, 4, 8, 16, "auto"] + criterion: ['gini'] # ['entropy'] + min_samples_split: [2] # [5, 10] + algorithm: ['SAMME'] # ['SAMME.R'] + learning_rate: [0.01] # [0.1, 1, 10, 100] + n_estimators_boost: [10] # [25, 50, 100, 1000, 10000] + ExtraTrees: + n_estimators: [ 10] # [25, 50, 100, 1000, 10000] + max_depth: [3 ] # 5, 10] # [20, 50, 100] + max_features: ['log2'] # [4, 8, 16, "auto"] + criterion: ['gini'] #, 'entropy'] + min_samples_split: [2] #, 5, 10] + AdaBoost: + algorithm: ['SAMME', 'SAMME.R'] + n_estimators: [1, 10, 100] # [1000, 10000] + learning_rate: [0.01, 0.1, 1, 10, 100] + LogisticRegression: + C_reg: [0.00001, 0.0001, 0.001, 0.01, 0.1] # [1, 10] + penalty: ['l1', 'l2'] + SVM: + C_reg: [0.00001, 0.0001, 0.001, 0.01, 0.1] # [1, 10] + kernel: ['linear'] + GradientBoostingClassifier: + n_estimators: [1, 10, 100] # [1000, 10000] + learning_rate: [0.001, 0.01, 0.05, 0.1, 0.5] + subsample: [0.1, 0.5, 1.0] + max_depth: [1, 3, 5, 10, 20] # [50, 100] + DecisionTreeClassifier: + criterion: ['gini', 'entropy'] + max_depth: [1, 5, 10, 20] # [50, 100] + max_features: ['sqrt', 'log2'] + min_samples_split: [2, 5, 10] + SGDClassifier: + loss: ['log', 'modified_huber'] + penalty: ['l1', 'l2', 'elasticnet'] + KNeighborsClassifier: + n_neighbors: [1, 3, 5, 10, 25, 50, 100] + weights: ['uniform', 'distance'] + algorithm: ['auto', 'kd_tree'] \ No newline at end of file From 42eba49363a7f5f80ab4becd22a617d1f216a01f Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 21 Sep 2016 23:15:18 -0700 Subject: [PATCH 09/27] Add database setup for model storage --- roles/database/files/create_schema_models.sql | 2 + ...eate_table_undefended_frontpage_models.sql | 65 +++++++++++++++++++ 2 files changed, 67 insertions(+) create mode 100644 roles/database/files/create_schema_models.sql create mode 100644 roles/database/files/create_table_undefended_frontpage_models.sql diff --git a/roles/database/files/create_schema_models.sql b/roles/database/files/create_schema_models.sql new file mode 100644 index 00000000..f6f7adb7 --- /dev/null +++ b/roles/database/files/create_schema_models.sql @@ -0,0 +1,2 @@ +DROP SCHEMA IF EXISTS models CASCADE; +CREATE SCHEMA models; diff --git a/roles/database/files/create_table_undefended_frontpage_models.sql b/roles/database/files/create_table_undefended_frontpage_models.sql new file mode 100644 index 00000000..49838851 --- /dev/null +++ b/roles/database/files/create_table_undefended_frontpage_models.sql @@ -0,0 +1,65 @@ +CREATE TABLE models.undefended_frontpage_attacks ( + modelid SERIAL PRIMARY KEY, + auc NUMERIC NOT NULL, + model_timestamp TIMESTAMP NOT NULL, + numfolds NUMERIC, + train_class_balance NUMERIC, + world_type VARCHAR(30), + model_type VARCHAR(100), + observed_world_fraction NUMERIC, + base_rate NUMERIC, + hyperparameters JSON NOT NULL, + tpr NUMERIC ARRAY, + fpr NUMERIC ARRAY, + precision_at_0_point_01_percent NUMERIC, + precision_at_0_point_05_percent NUMERIC, + precision_at_0_point_1_percent NUMERIC, + precision_at_0_point_5_percent NUMERIC, + precision_at_1_percent NUMERIC, + precision_at_5_percent NUMERIC, + precision_at_10_percent NUMERIC, + recall_at_0_point_01_percent NUMERIC, + recall_at_0_point_05_percent NUMERIC, + recall_at_0_point_1_percent NUMERIC, + recall_at_0_point_5_percent NUMERIC, + recall_at_1_percent NUMERIC, + recall_at_5_percent NUMERIC, + recall_at_10_percent NUMERIC, + f1_at_0_point_01_percent NUMERIC, + f1_at_0_point_05_percent NUMERIC, + f1_at_0_point_1_percent NUMERIC, + f1_at_0_point_5_percent NUMERIC, + f1_at_1_percent NUMERIC, + f1_at_5_percent NUMERIC, + f1_at_10_percent NUMERIC +); + +CREATE TABLE models.undefended_frontpage_folds ( + foldid SERIAL PRIMARY KEY, + model_timestamp TIMESTAMP NOT NULL, + fold_timestamp TIMESTAMP NOT NULL, + auc NUMERIC NOT NULL, + tpr NUMERIC ARRAY, + fpr NUMERIC ARRAY, + precision_at_0_point_01_percent NUMERIC, + precision_at_0_point_05_percent NUMERIC, + precision_at_0_point_1_percent NUMERIC, + precision_at_0_point_5_percent NUMERIC, + precision_at_1_percent NUMERIC, + precision_at_5_percent NUMERIC, + precision_at_10_percent NUMERIC, + recall_at_0_point_01_percent NUMERIC, + recall_at_0_point_05_percent NUMERIC, + recall_at_0_point_1_percent NUMERIC, + recall_at_0_point_5_percent NUMERIC, + recall_at_1_percent NUMERIC, + recall_at_5_percent NUMERIC, + recall_at_10_percent NUMERIC, + f1_at_0_point_01_percent NUMERIC, + f1_at_0_point_05_percent NUMERIC, + f1_at_0_point_1_percent NUMERIC, + f1_at_0_point_5_percent NUMERIC, + f1_at_1_percent NUMERIC, + f1_at_5_percent NUMERIC, + f1_at_10_percent NUMERIC +); \ No newline at end of file From e25c95006f57c4c82000ed1b3f66f673b86e6a78 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 21 Sep 2016 23:16:54 -0700 Subject: [PATCH 10/27] Add model storage to database.py --- fpsd/database.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/fpsd/database.py b/fpsd/database.py index 46dc2c5d..ff75f860 100644 --- a/fpsd/database.py +++ b/fpsd/database.py @@ -2,6 +2,7 @@ from collections import OrderedDict from contextlib import contextmanager from datetime import datetime as dt +import json import os import pandas as pd import re @@ -203,3 +204,34 @@ def load_closed_world(self): df = pd.read_sql(labelled_query, self.engine) return df + + def load_open_world(self): + """For open world validation, we must keep track of which onion service + a trace came from + + Returns: + df [pandas DataFrame]: dataset with hs_url + """ + + labelled_query = ('select t1.*, t3.is_sd, t3.hs_url ' + 'from features.frontpage_features t1 ' + 'inner join raw.frontpage_examples t2 ' + 'on t1.exampleid = t2.exampleid ' + 'inner join raw.hs_history t3 ' + 'on t3.hsid = t2.hsid') + + df = pd.read_sql(labelled_query, self.engine) + return df + + +class ModelStorage(Database): + """Store trained models in the database""" + def __init__(self): + super().__init__(**kwargs) + + def save_model(self, auc, config, timestamp): + query = ("INSERT INTO models.undefended_frontpage_models (auc, config, t_generation) " + "VALUES ({}, '{}', '{}') ".format(auc, json.dumps(config), timestamp)) + with safe_session(self.engine) as session: + session.execute(query) + return None \ No newline at end of file From c578a79281c105704360fae971462a145e3f340d Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Fri, 23 Sep 2016 16:19:21 -0700 Subject: [PATCH 11/27] Add observed_fraction to open world validation --- fpsd/attack.yaml | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/fpsd/attack.yaml b/fpsd/attack.yaml index b07c3139..56193b1e 100644 --- a/fpsd/attack.yaml +++ b/fpsd/attack.yaml @@ -2,7 +2,10 @@ # Train/Test # ################# -evaluation: 'closed' # or 'open' +world: + type: 'open' # 'closed' or 'open' + observed_fraction: 0.20 # fraction of the open world that can be measured by the adversary +num_kfolds: 10 ################# @@ -14,12 +17,13 @@ evaluation: 'closed' # or 'open' # 'AdaBoost', 'LogisticRegression', 'SVM', 'GradientBoostingClassifier', # 'DecisionTreeClassifier', 'SGDClassifier', 'KNeighborsClassifier'] -models: ['RandomForest'] +models: ['RandomForest', 'ExtraTrees', 'DecisionTreeClassifier', 'RandomForestBagging', + 'RandomForestBoosting'] parameters: RandomForest: - n_estimators: [10, 25, 50] # 100, 1000, 10000 - max_depth: [5, 10, 20] # 50, 100 - max_features: ['sqrt'] # ['log2', 2, 4, 8, 16, "auto"] + n_estimators: [25, 50, 100] #[25, 50] # 100, 1000, 10000, 10 + max_depth: [10, 20, 50, 100] # 50, 100, 5 + max_features: ['sqrt', 'log2', 2, 4, 8, 16, "auto"] criterion: ['gini', 'entropy'] min_samples_split: [2, 5, 10] RandomForestBagging: From efe2732ce7339d53960ab5c68fd1fdff83dd818a Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Fri, 23 Sep 2016 17:20:26 -0700 Subject: [PATCH 12/27] Add requirements for machine learning codes --- fpsd/requirements/ml-requirements.txt | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 fpsd/requirements/ml-requirements.txt diff --git a/fpsd/requirements/ml-requirements.txt b/fpsd/requirements/ml-requirements.txt new file mode 100644 index 00000000..2a6398c5 --- /dev/null +++ b/fpsd/requirements/ml-requirements.txt @@ -0,0 +1,14 @@ +ipython==5.1.0 +jupyter==1.0.0 +luigi==2.3.1 +matplotlib==1.5.2 +numpy==1.11.1 +pandas==0.18.1 +psycopg2==2.6.2 +PyYAML==3.12 +scikit-learn==0.17.1 +scipy==0.18.1 +seaborn==0.7.1 +SQLAlchemy==1.0.14 +tqdm==4.8.4 + From 819d6a2a0f13d656156c78687c84ea9b9665d36f Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Fri, 7 Oct 2016 17:53:59 -0700 Subject: [PATCH 13/27] Add little guide describing how to integrate a new classifier into the analysis pipeline --- CONTRIB.md | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 CONTRIB.md diff --git a/CONTRIB.md b/CONTRIB.md new file mode 100644 index 00000000..fa51db3e --- /dev/null +++ b/CONTRIB.md @@ -0,0 +1,9 @@ +# Adding Features + +Writing a new feature can be done by adding a method in `fpsd/features.py`. + +# Adding Classifiers + +A new classifier can be added by adding a new stanza in the `Experiment._get_model_object()` method in `classify.py`. This method must return an object that has the `fit()` - model training - and `predict_proba()` - predict scores on test set - methods defined on it. It expects these methods because that is what scikit-learn uses for its classifier objects. + +To get the code to use the classifier, add a string corresponding to its name to your attack YAML file under `models` and add any hyperparameters and options (e.g. number of rounds for Wa-kNN) under `parameters`. \ No newline at end of file From e1ecf561406afbd63fd66a9e2a7a60fdd9adcb49 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Mon, 10 Oct 2016 14:05:49 -0700 Subject: [PATCH 14/27] Add feature scaling option to attack setup --- fpsd/attack.yaml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/fpsd/attack.yaml b/fpsd/attack.yaml index 56193b1e..513eaa44 100644 --- a/fpsd/attack.yaml +++ b/fpsd/attack.yaml @@ -8,6 +8,15 @@ world: num_kfolds: 10 + +###################### +# Preprocessing # +###################### + +feature_scaling: True # Rescale each feature to mean zero and unit standard deviation + + + ################# # Classifiers # ################# From 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zJSZ+U*vz{3S^=C%yv=D;l{cQ8W2fr?*ht|VX;pv^jg%(&#Xv&C>ChM#LcBS0?RXo} z(1}4L)iVj7^jClF`kxyBdu-VSa4htH8Nj~`D}@wD?N%^-V~YPa-haG&0Reo@+TPFXzrW_Mj~D{M94LgC=Y#%n$iY9}_WwR~)x#(-N061*e>?0j zh3S8OqcY5CY7kKCmq-8oHGlnOixr0KnuI2k|8Btl>lgp=zyI%I|9g!5?_&SkLiyjy o{`V02UmE%MT>Jkwja(A4Z80{XZ@53a0({9!t2{4zX8Q8~0eE)AGynhq literal 0 HcmV?d00001 From 42e272c228d9ba03e181c6594d8d43962471ceb3 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Mon, 10 Oct 2016 14:09:44 -0700 Subject: [PATCH 16/27] Add writeup/readme on how pipeline works --- docs/Pipeline.md | 85 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 docs/Pipeline.md diff --git a/docs/Pipeline.md b/docs/Pipeline.md new file mode 100644 index 00000000..d722370e --- /dev/null +++ b/docs/Pipeline.md @@ -0,0 +1,85 @@ +# Web Fingerprinting Analysis Pipeline + +![](images/pipeline.png) + +## Feature Generation + +Our feature generation code is primarily in SQL and takes data the crawlers dump into the `raw` schema, generates all features relevant to Tor traffic from Wang et al. 2014, and stores the results in the `features` schema: + +![](images/feature_generation.png) + +Run this step with: + +```python features.py``` + +## Machine Learning + +This step: + +* takes the features in the database, +* trains a series of binary classifiers, +* evaluates how well each classifier performs, +* and then saves performance metrics in the database as well as pickling the trained model objects (for use in future scoring) + +Run this step with: + +```python attack.py -a my_attack_file.yaml``` + +### Attack Setup + +The machine learning part of the code takes a YAML file (by default `attack.yaml`) as input to specify details of the models that should be generated. Here are the options that are currently implemented: + +* `world`: specifies what kind of cross validation should be performed. + * `type`: `closed` or `open` + * `observed_fraction`: specifies the fraction of the world that is "observed" (measured by the adversary) for open world validation. + +* `num_kfolds`: value of k for k-fold cross-validation + +* `feature_scaling`: this option will take the features and rescale them to a zero mean and unit standard deviation. For some classifiers, e.g. primarily those based on decision trees, this should not improve performance, but for many classifiers, e.g. SVM, this is necessary. + +* `models`: a list of types of binary classifiers that should be trained + +* `parameters`: this option specifies the range of hyperparameters that should be used for each classifier type + +For more details and examples, see `attack.yaml` as an example. + +### Model Training and Evaluation + +When this step of the pipeline runs, it will: + +* get the features from the database, +* split the data into train/test sets, +* generate a series of experiments to be run that tries every possible combination of preprocessing option, model type and hyperparameter set +* for each experiment: + * for every train/test split, it will: + * train on the training set, + * evaluate on the testing set, + * save the metrics in the database in `models.undefended_frontpage_folds`, + * pickle the trained model and save for future scoring + * average the metrics for the folds from that experiment and save them in the database in `models.undefended_frontpage_attacks`. + +### Evaluation and Output in Model Schema + +The information and evaluation metrics stored in the database in table `models.undefended_frontpage_folds`: + +* `auc`: [Area under the ROC curve](http://people.inf.elte.hu/kiss/12dwhdm/roc.pdf) +* `tpr`: true positive rate [array for default sklearn thresholds] +* `fpr`: false positive rate [array for default sklearn thresholds] +* `precision_at_k` for `k=[0.01, 0.05, 0.1, 0.5, 1, 5, 10]`: "Fraction of SecureDrop users correctly identified in the top k percent" +* `recall_at_k` for `k=[0.01, 0.05, 0.1, 0.5, 1, 5, 10]`: "Number of SecureDrop users captured by flagging the top k percent" +* `f1_at_k` for `k=[0.01, 0.05, 0.1, 0.5, 1, 5, 10]` + +The same metrics are then computed over all folds and saved in `models.undefended_frontpage_attacks`, in addition to: + +* `world_type` +* `train_class_balance` +* `base_rate` (test class balance) +* `observed_world_size` if in open world validation +* `model_type` +* `hyperparameters` in json format + +The `model_timestamp` and `fold_timestamp` are saved as identifiers in `models.undefended_frontpage_folds` and the `model_timestamp` is saved in `models.undefended_frontpage_attacks`. + +## Model Selection + +This is currently done manually by selecting the top `auc` model in the database. \ No newline at end of file From c365bbd6cb1dad50a2084356e6f1ba3fa4575d00 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Tue, 11 Oct 2016 09:57:50 -0700 Subject: [PATCH 17/27] Add tests for custom evaluation code --- fpsd/test/test_evaluation.py | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 fpsd/test/test_evaluation.py diff --git a/fpsd/test/test_evaluation.py b/fpsd/test/test_evaluation.py new file mode 100644 index 00000000..b855d830 --- /dev/null +++ b/fpsd/test/test_evaluation.py @@ -0,0 +1,35 @@ +import unittest + +from evaluation import precision_recall_at_x_proportion + + +class EvaluationTest(unittest.TestCase): + """Most evaluation methods are from scikit-learn and are thus tested + however some of our preprocessing is custom and should be tested""" + + def test_precision_recall_f1_perfect(self): + test_labels = [1, 1, 0, 0] + test_predictions = [0.99, 0.99, 0.01, 0.01] + precision, recall, f1 = precision_recall_at_x_proportion(test_labels, + test_predictions, x_proportion=0.50) + self.assertEqual(recall, 1) + self.assertEqual(precision, 1) + self.assertEqual(f1, 1) + + def test_precision_recall_f1_horrible(self): + test_labels = [0, 0, 1, 1] + test_predictions = [0.99, 0.99, 0.01, 0.01] + precision, recall, f1 = precision_recall_at_x_proportion(test_labels, + test_predictions, x_proportion=0.50) + self.assertEqual(recall, 0) + self.assertEqual(precision, 0) + self.assertEqual(f1, 0) + + def test_precision_recall_f1_realistic(self): + test_labels = [1, 0, 1, 0] + test_predictions = [0.80, 0.20, 0.20, 0.80] + precision, recall, f1 = precision_recall_at_x_proportion(test_labels, + test_predictions, x_proportion=0.50) + self.assertEqual(recall, 0.5) + self.assertEqual(precision, 0.5) + self.assertEqual(f1, 0.5) \ No newline at end of file From 286529fac72b3aac0eec9f0f3d2147eb3fe0ae39 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Tue, 11 Oct 2016 09:58:28 -0700 Subject: [PATCH 18/27] Add a ton of evaluation metrics to the models table --- fpsd/database.py | 66 +++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 60 insertions(+), 6 deletions(-) diff --git a/fpsd/database.py b/fpsd/database.py index ff75f860..6549572b 100644 --- a/fpsd/database.py +++ b/fpsd/database.py @@ -76,7 +76,6 @@ def __init__(self, **kwargs): self.Cell = Base.classes.frontpage_traces self.Crawl = Base.classes.crawls - def _wipe_raw_schema(self): """Like with a cloth. Delete entries while keeping table structure intact.""" @@ -84,7 +83,6 @@ def _wipe_raw_schema(self): for table in self.Cell, self.Example, self.Onion, self.Crawl: session.query(table).delete() - def add_onions(self, class_data): """Add sorted onions into the HS history table""" onions = [] @@ -228,10 +226,66 @@ class ModelStorage(Database): """Store trained models in the database""" def __init__(self): super().__init__(**kwargs) + self.COMMON_METRICS = ("auc, tpr, fpr, " + "precision_at_0_point_01_percent, " + "precision_at_0_point_05_percent, " + "precision_at_0_point_1_percent, " + "precision_at_0_point_5_percent, " + "precision_at_1_percent, precision_at_5_percent, " + "precision_at_10_percent, " + "recall_at_0_point_01_percent, " + "recall_at_0_point_05_percent, " + "recall_at_0_point_1_percent, " + "recall_at_0_point_5_percent, " + "recall_at_1_percent, recall_at_5_percent, " + "recall_at_10_percent, f1_at_0_point_01_percent, " + "f1_at_0_point_05_percent, f1_at_0_point_1_percent, " + "f1_at_0_point_5_percent, f1_at_1_percent, " + "f1_at_5_percent, f1_at_10_percent") + + def metric_formatter(self, metrics): + """Format the metrics query""" + for metric in ("tpr", "fpr"): + metrics[metric] = [str(x) for x in metrics[metric]] + #metrics[metric] = ["'{}'".format(x) for x in metrics[metric]] + metrics[metric] = "'{{ {} }}'".format(", ".join(metrics[metric])) + metrics_list = [metrics["auc"], + metrics["tpr"], metrics["fpr"], + metrics[0.01]["precision"], + metrics[0.05]["precision"], metrics[0.1]["precision"], + metrics[0.5]["precision"], metrics[1]["precision"], + metrics[5]["precision"], metrics[10]["precision"], + metrics[0.01]["recall"], + metrics[0.05]["recall"], metrics[0.1]["recall"], + metrics[0.5]["recall"], metrics[1]["recall"], + metrics[5]["recall"], metrics[10]["recall"], + metrics[0.01]["f1"], + metrics[0.05]["f1"], metrics[0.1]["f1"], + metrics[0.5]["f1"], metrics[1]["f1"], + metrics[5]["f1"], metrics[10]["f1"]] + metrics_list = [str(x) for x in metrics_list] + + return ', '.join(metrics_list) + + def save_full_model(self, eval_metrics, model_timestamp, options): + query = ("INSERT INTO models.undefended_frontpage_attacks " + "(model_timestamp, numfolds, " + "train_class_balance, world_type, model_type, " + "base_rate, hyperparameters, {}) " + "VALUES ('{}', {}, {}, '{}', '{}', {}, '{}', {} " + ") ".format(self.COMMON_METRICS, model_timestamp, + options["numfolds"], options["train_class_balance"], + options["world_type"], options["model_type"], + options["base_rate"], json.dumps(options["hyperparameters"]), + self.metric_formatter(eval_metrics))) + with safe_session(self.engine) as session: + session.execute(query) - def save_model(self, auc, config, timestamp): - query = ("INSERT INTO models.undefended_frontpage_models (auc, config, t_generation) " - "VALUES ({}, '{}', '{}') ".format(auc, json.dumps(config), timestamp)) + def save_fold_of_model(self, eval_metrics, model_timestamp, fold_timestamp): + query = ("INSERT INTO models.undefended_frontpage_folds " + "(model_timestamp, fold_timestamp, {}) VALUES " + "('{}', '{}', {}) ".format(self.COMMON_METRICS, + model_timestamp, fold_timestamp, + self.metric_formatter(eval_metrics))) with safe_session(self.engine) as session: session.execute(query) - return None \ No newline at end of file From 01dd6b955c80ec3fc3c4219f145c77aa26c0e745 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 16 Nov 2016 14:00:16 -0800 Subject: [PATCH 19/27] Feedback from code review --- docs/Pipeline.md | 10 ++--- fpsd/attack.py | 20 ++++----- fpsd/attack.yaml | 5 ++- fpsd/classify.py | 24 +++++----- fpsd/database.py | 45 +++++++------------ fpsd/evaluation.py | 106 +++++++++++++++++++++++++-------------------- 6 files changed, 103 insertions(+), 107 deletions(-) diff --git a/docs/Pipeline.md b/docs/Pipeline.md index d722370e..15b71333 100644 --- a/docs/Pipeline.md +++ b/docs/Pipeline.md @@ -10,7 +10,7 @@ Our feature generation code is primarily in SQL and takes data the crawlers dump Run this step with: -```python features.py``` +```./features.py``` ## Machine Learning @@ -23,7 +23,7 @@ This step: Run this step with: -```python attack.py -a my_attack_file.yaml``` +```./attack.py -a my_attack_file.yaml``` ### Attack Setup @@ -35,7 +35,7 @@ The machine learning part of the code takes a YAML file (by default `attack.yaml * `num_kfolds`: value of k for k-fold cross-validation -* `feature_scaling`: this option will take the features and rescale them to a zero mean and unit standard deviation. For some classifiers, e.g. primarily those based on decision trees, this should not improve performance, but for many classifiers, e.g. SVM, this is necessary. +* `feature_scaling`: this option will take the features and [rescale](https://en.wikipedia.org/wiki/Feature_scaling) them to a [zero mean and unit standard deviation](https://en.wikipedia.org/wiki/Standard_score). For some classifiers, e.g. primarily those based on decision trees, this should not improve performance, but for many classifiers, e.g. SVM, this is necessary. See also [scikit-learn's documentation](http://scikit-learn.org/stable/modules/preprocessing.html). * `models`: a list of types of binary classifiers that should be trained @@ -65,8 +65,8 @@ The information and evaluation metrics stored in the database in table `models.u * `auc`: [Area under the ROC curve](http://people.inf.elte.hu/kiss/12dwhdm/roc.pdf) * `tpr`: true positive rate [array for default sklearn thresholds] * `fpr`: false positive rate [array for default sklearn thresholds] -* `precision_at_k` for `k=[0.01, 0.05, 0.1, 0.5, 1, 5, 10]`: "Fraction of SecureDrop users correctly identified in the top k percent" -* `recall_at_k` for `k=[0.01, 0.05, 0.1, 0.5, 1, 5, 10]`: "Number of SecureDrop users captured by flagging the top k percent" +* `precision_at_k` for `k=[0.01, 0.05, 0.1, 0.5, 1, 5, 10]`: "Fraction of SecureDrop users correctly identified in the top k percent of the testing set" +* `recall_at_k` for `k=[0.01, 0.05, 0.1, 0.5, 1, 5, 10]`: "Number of SecureDrop users captured by flagging the top k percent of the testing set" * `f1_at_k` for `k=[0.01, 0.05, 0.1, 0.5, 1, 5, 10]` The same metrics are then computed over all folds and saved in `models.undefended_frontpage_attacks`, in addition to: diff --git a/fpsd/attack.py b/fpsd/attack.py index cafce1a4..383733e3 100644 --- a/fpsd/attack.py +++ b/fpsd/attack.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3.5 import argparse import datetime from itertools import product @@ -21,17 +22,13 @@ def run(options): db = database.DatasetLoader() - if options["world"]["type"] == "closed": - df = db.load_closed_world() - elif options["world"]["type"] == "open": - df = db.load_open_world() + df = db.load_world(options["world"]["type"]) - df = classify.interpolate(df) + df = classify.imputation(df) x = df.drop(['exampleid', 'is_sd'], axis=1).values y = df['is_sd'].astype(int).values - experiments = generate_experiments(options) - for experiment in experiments: + for experiment in generate_experiments(options): experiment.train_eval_all_folds(x, y) @@ -53,6 +50,8 @@ def generate_experiments(options): parameter_names = sorted(model_hyperparameters) parameter_values = [model_hyperparameters[p] for p in parameter_names] + + # Compute Cartesian product of hyperparameter lists all_params = product(*parameter_values) for param in all_params: @@ -72,8 +71,9 @@ def generate_experiments(options): if __name__=='__main__': parser = argparse.ArgumentParser() - parser.add_argument("-a", "--setup-yml", dest="attack_setup", type=str, default="attack.yaml", - help="point to attack setup file") + parser.add_argument("-c", "--config", dest="config", type=str, + default="attack.yaml", + help="point to attack config/setup file") args = parser.parse_args() - run(args.attack_setup) + run(args.config) diff --git a/fpsd/attack.yaml b/fpsd/attack.yaml index 513eaa44..3651d443 100644 --- a/fpsd/attack.yaml +++ b/fpsd/attack.yaml @@ -3,7 +3,7 @@ ################# world: - type: 'open' # 'closed' or 'open' + type: 'closed' # 'closed' or 'open' observed_fraction: 0.20 # fraction of the open world that can be measured by the adversary num_kfolds: 10 @@ -87,4 +87,5 @@ parameters: KNeighborsClassifier: n_neighbors: [1, 3, 5, 10, 25, 50, 100] weights: ['uniform', 'distance'] - algorithm: ['auto', 'kd_tree'] \ No newline at end of file + algorithm: ['auto', 'kd_tree'] + diff --git a/fpsd/classify.py b/fpsd/classify.py index 9b54c6da..b115cc01 100644 --- a/fpsd/classify.py +++ b/fpsd/classify.py @@ -1,8 +1,8 @@ import datetime import matplotlib.pyplot as plt import numpy as np +import multiprocessing import pickle -from scipy import interp from sklearn import (cross_validation, ensemble, metrics, svm, tree, linear_model, neighbors, naive_bayes, preprocessing) @@ -11,7 +11,7 @@ import evaluation, database -def interpolate(df): +def imputation(df): """Handle missing values in our data. This is mostly a placeholder for when we have a better way to handle this. @@ -25,10 +25,10 @@ def interpolate(df): return df.fillna(0) -class Experiment(object): +class Experiment: def __init__(self, model_timestamp, world, model_type, - hyperparameters, - feature_scaling=True, n_cores=2, k=10): + hyperparameters, feature_scaling=True, + n_cores=multiprocessing.cpu_count(), k=10): """ Args: model [string]: machine learning algorithm to be used @@ -60,12 +60,12 @@ def get_dict(self): "train_class_balance": 'DEFAULT', "base_rate": 'DEFAULT'} - def train_single_fold(self, train_x, train_y): + def train_single_fold(self, x_train, y_train): """Trains a model and saves it as self.trained_model Args: - train_x [ndarray]: features in training set (no id column, no target) - train_y [ndarray]: target variable in training set (no id column) + x_train [ndarray]: features in training set (no id column, no target) + y_train [ndarray]: target variable in training set (no id column) """ print("Training {} classifier with {}".format(self.model_type, @@ -74,21 +74,21 @@ def train_single_fold(self, train_x, train_y): modelobj = self._get_model_object(self.model_type, self.hyperparameters, self.n_cores) - trained_model = modelobj.fit(train_x, train_y) + trained_model = modelobj.fit(x_train, y_train) return trained_model - def score(self, test_x, trained_model): + def score(self, x_test, trained_model): """Generates continuous risk scores for a testing set. Args: - test_x [ndarray]: testing features + x_test [ndarray]: testing features trained_model [sklearn object]: trained classifier object Returns: result_y [ndarray]: predictions on test set """ - result_y = trained_model.predict_proba(test_x) + result_y = trained_model.predict_proba(x_test) score_positive_class = result_y[:, 1] return score_positive_class diff --git a/fpsd/database.py b/fpsd/database.py index 6549572b..111edf2b 100644 --- a/fpsd/database.py +++ b/fpsd/database.py @@ -183,48 +183,33 @@ def add_trace(self, trace, exampleid): class DatasetLoader(Database): """Load train/test sets""" - def __init__(self): + def __init__(self, **kwargs): super().__init__(**kwargs) - def load_closed_world(self): - """For closed world validation, we can select traces - without consideration of which site they belong to and instead + def load_world(self, world_type): + """For open world validation, we must keep track of which onion service + a trace came from. However for closed world validation, we can select + traces without consideration of which site they belong to. - Returns: - df [pandas DataFrame]: dataset + :returns: a pandas DataFrame df containing the dataset """ - labelled_query = ('select t1.*, t3.is_sd from features.frontpage_features t1 ' + select_hs_urls = ', t3.hs_url' if world_type is 'open' else '' + + labeled_query = ('select t1.*, t3.is_sd {} ' + 'from features.frontpage_features t1 ' 'inner join raw.frontpage_examples t2 ' 'on t1.exampleid = t2.exampleid ' 'inner join raw.hs_history t3 ' - 'on t3.hsid = t2.hsid') + 'on t3.hsid = t2.hsid').format(world_type) - df = pd.read_sql(labelled_query, self.engine) - return df - - def load_open_world(self): - """For open world validation, we must keep track of which onion service - a trace came from - - Returns: - df [pandas DataFrame]: dataset with hs_url - """ - - labelled_query = ('select t1.*, t3.is_sd, t3.hs_url ' - 'from features.frontpage_features t1 ' - 'inner join raw.frontpage_examples t2 ' - 'on t1.exampleid = t2.exampleid ' - 'inner join raw.hs_history t3 ' - 'on t3.hsid = t2.hsid') - - df = pd.read_sql(labelled_query, self.engine) + df = pd.read_sql(labeled_query, self.engine) return df class ModelStorage(Database): """Store trained models in the database""" - def __init__(self): + def __init__(self, **kwargs): super().__init__(**kwargs) self.COMMON_METRICS = ("auc, tpr, fpr, " "precision_at_0_point_01_percent, " @@ -278,7 +263,7 @@ def save_full_model(self, eval_metrics, model_timestamp, options): options["world_type"], options["model_type"], options["base_rate"], json.dumps(options["hyperparameters"]), self.metric_formatter(eval_metrics))) - with safe_session(self.engine) as session: + with self.safe_session() as session: session.execute(query) def save_fold_of_model(self, eval_metrics, model_timestamp, fold_timestamp): @@ -287,5 +272,5 @@ def save_fold_of_model(self, eval_metrics, model_timestamp, fold_timestamp): "('{}', '{}', {}) ".format(self.COMMON_METRICS, model_timestamp, fold_timestamp, self.metric_formatter(eval_metrics))) - with safe_session(self.engine) as session: + with self.safe_session() as session: session.execute(query) diff --git a/fpsd/evaluation.py b/fpsd/evaluation.py index 63cbd9c5..2a743395 100644 --- a/fpsd/evaluation.py +++ b/fpsd/evaluation.py @@ -28,13 +28,13 @@ def get_metrics(truth, predicted): eval_metrics["tpr"] = tpr eval_metrics["thresholds"] = thresholds eval_metrics["auc"] = metrics.auc(fpr, tpr) - eval_metrics["precision"] = {} - eval_metrics["recall"] = {} - eval_metrics["f1"] = {} + eval_metrics["precision"] = {} # TODO + eval_metrics["recall"] = {} # TODO + eval_metrics["f1"] = {} # TODO for threshold in THRESHOLDS: precision, recall, f1 = precision_recall_at_x_proportion(truth, - predicted, x_proportion=threshold/100.) + predicted, x_proportion=threshold/100) eval_metrics.update({threshold: {'precision': precision, 'recall': recall, 'f1': f1}}) @@ -57,7 +57,7 @@ def get_average_metrics(metrics_list): for metric in ("fpr", "tpr"): # the fpr, tpr output from scikit-learn may not have the same # number of elements in the arrays, set to Null for now - eval_metrics.update({metric: [0, 0]}) + eval_metrics.update({metric: [0, 0]}) # TODO for threshold in THRESHOLDS: eval_metrics[threshold] = {} @@ -73,50 +73,18 @@ def get_average_metrics(metrics_list): return eval_metrics -def get_feature_importances(model): - - try: - return model.feature_importances_ - except: - pass - try: - # Must be 1D for feature importance plot - if len(model.coef_) <= 1: - return model.coef_[0] - else: - return model.coef_ - except: - pass - return None - - -def plot_feature_importances(feature_names, feature_importances, N=30): - importances = list(zip(feature_names, list(feature_importances))) - importances = pd.DataFrame(importances, columns=["Feature", "Importance"]) - importances = importances.set_index("Feature") - - # Sort by the absolute value of the importance of the feature - importances["sort"] = abs(importances["Importance"]) - importances = importances.sort(columns="sort", ascending=False).drop("sort", axis=1) - importances = importances[0:N] - - # Show the most important positive feature at the top of the graph - importances = importances.sort(columns="Importance", ascending=True) - - with plt.style.context(('ggplot')): - fig, ax = plt.subplots(figsize=(16,12)) - ax.tick_params(labelsize=16) - importances.plot(kind="barh", legend=False, ax=ax) - ax.set_frame_on(False) - ax.set_xlabel("Relative importance", fontsize=20) - ax.set_ylabel("Feature name", fontsize=20) - plt.tight_layout() - plt.title("Most important features for attack", fontsize=20).set_position([.5, 0.99]) - return fig - - def precision_recall_at_x_proportion(test_labels, test_predictions, x_proportion=0.01, return_cutoff=False): + """Compute precision, recall, F1 for a specified fraction of the test set. + + :params list test_labels: true labels on test set + :params list test_predicted: predicted labels on test set + :params float x_proportion: proportion of the test set to flag + :params bool return_cutoff: if True return the cutoff probablility + :returns float precision: fraction correctly flagged + :returns float recall: fraction of the positive class recovered + :returns float f1: + """ cutoff_index = int(len(test_predictions) * x_proportion) cutoff_index = min(cutoff_index, len(test_predictions) - 1) @@ -190,4 +158,46 @@ def plot_allkfolds_ROC(timestamp, cv, fpr_arr, tpr_arr): ax.legend(loc="lower right") plt.savefig('{}_roc.png'.format(timestamp)) plt.close('all') - return mean_auc \ No newline at end of file + return mean_auc + + +def get_feature_importances(model): + + try: + return model.feature_importances_ + except: + pass + try: + # Must be 1D for feature importance plot + if len(model.coef_) <= 1: + return model.coef_[0] + else: + return model.coef_ + except: + pass + return None + + +def plot_feature_importances(feature_names, feature_importances, N=30): + importances = list(zip(feature_names, list(feature_importances))) + importances = pd.DataFrame(importances, columns=["Feature", "Importance"]) + importances = importances.set_index("Feature") + + # Sort by the absolute value of the importance of the feature + importances["sort"] = abs(importances["Importance"]) + importances = importances.sort(columns="sort", ascending=False).drop("sort", axis=1) + importances = importances[0:N] + + # Show the most important positive feature at the top of the graph + importances = importances.sort(columns="Importance", ascending=True) + + with plt.style.context(('ggplot')): + fig, ax = plt.subplots(figsize=(16,12)) + ax.tick_params(labelsize=16) + importances.plot(kind="barh", legend=False, ax=ax) + ax.set_frame_on(False) + ax.set_xlabel("Relative importance", fontsize=20) + ax.set_ylabel("Feature name", fontsize=20) + plt.tight_layout() + plt.title("Most important features for attack", fontsize=20).set_position([.5, 0.99]) + return fig \ No newline at end of file From 09559161a5c178941557e94f7c81b61dbd8f507f Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 16 Nov 2016 16:24:37 -0800 Subject: [PATCH 20/27] Move requirements --- fpsd/requirements/ml-requirements.txt | 14 -------------- fpsd/requirements/requirements.in | 8 +++++++- 2 files changed, 7 insertions(+), 15 deletions(-) delete mode 100644 fpsd/requirements/ml-requirements.txt diff --git a/fpsd/requirements/ml-requirements.txt b/fpsd/requirements/ml-requirements.txt deleted file mode 100644 index 2a6398c5..00000000 --- a/fpsd/requirements/ml-requirements.txt +++ /dev/null @@ -1,14 +0,0 @@ -ipython==5.1.0 -jupyter==1.0.0 -luigi==2.3.1 -matplotlib==1.5.2 -numpy==1.11.1 -pandas==0.18.1 -psycopg2==2.6.2 -PyYAML==3.12 -scikit-learn==0.17.1 -scipy==0.18.1 -seaborn==0.7.1 -SQLAlchemy==1.0.14 -tqdm==4.8.4 - diff --git a/fpsd/requirements/requirements.in b/fpsd/requirements/requirements.in index 58ffd9f2..ac248611 100644 --- a/fpsd/requirements/requirements.in +++ b/fpsd/requirements/requirements.in @@ -10,6 +10,12 @@ psycopg2 SQLAlchemy # Machine Learning +numpy +PyYAML pandas +scipy tqdm - +scikit-learn==0.17.1 +matplotlib +seaborn +jupyter From 2c2b442b5f12237893deb41c5ac132fa40275cbf Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 16 Nov 2016 17:00:43 -0800 Subject: [PATCH 21/27] Add Ansible configuration of models schema --- ...te_table_undefended_frontpage_attacks.sql} | 30 ------------------- ...reate_table_undefended_frontpage_folds.sql | 29 ++++++++++++++++++ roles/crawler/tasks/configure-databases.yml | 22 ++++++++++++-- roles/database/files/create_schema_models.sql | 2 -- 4 files changed, 49 insertions(+), 34 deletions(-) rename roles/{database/files/create_table_undefended_frontpage_models.sql => crawler/files/postgres-schemas/create_table_undefended_frontpage_attacks.sql} (53%) create mode 100644 roles/crawler/files/postgres-schemas/create_table_undefended_frontpage_folds.sql delete mode 100644 roles/database/files/create_schema_models.sql diff --git a/roles/database/files/create_table_undefended_frontpage_models.sql b/roles/crawler/files/postgres-schemas/create_table_undefended_frontpage_attacks.sql similarity index 53% rename from roles/database/files/create_table_undefended_frontpage_models.sql rename to roles/crawler/files/postgres-schemas/create_table_undefended_frontpage_attacks.sql index 49838851..54d9fd5c 100644 --- a/roles/database/files/create_table_undefended_frontpage_models.sql +++ b/roles/crawler/files/postgres-schemas/create_table_undefended_frontpage_attacks.sql @@ -33,33 +33,3 @@ CREATE TABLE models.undefended_frontpage_attacks ( f1_at_5_percent NUMERIC, f1_at_10_percent NUMERIC ); - -CREATE TABLE models.undefended_frontpage_folds ( - foldid SERIAL PRIMARY KEY, - model_timestamp TIMESTAMP NOT NULL, - fold_timestamp TIMESTAMP NOT NULL, - auc NUMERIC NOT NULL, - tpr NUMERIC ARRAY, - fpr NUMERIC ARRAY, - precision_at_0_point_01_percent NUMERIC, - precision_at_0_point_05_percent NUMERIC, - precision_at_0_point_1_percent NUMERIC, - precision_at_0_point_5_percent NUMERIC, - precision_at_1_percent NUMERIC, - precision_at_5_percent NUMERIC, - precision_at_10_percent NUMERIC, - recall_at_0_point_01_percent NUMERIC, - recall_at_0_point_05_percent NUMERIC, - recall_at_0_point_1_percent NUMERIC, - recall_at_0_point_5_percent NUMERIC, - recall_at_1_percent NUMERIC, - recall_at_5_percent NUMERIC, - recall_at_10_percent NUMERIC, - f1_at_0_point_01_percent NUMERIC, - f1_at_0_point_05_percent NUMERIC, - f1_at_0_point_1_percent NUMERIC, - f1_at_0_point_5_percent NUMERIC, - f1_at_1_percent NUMERIC, - f1_at_5_percent NUMERIC, - f1_at_10_percent NUMERIC -); \ No newline at end of file diff --git a/roles/crawler/files/postgres-schemas/create_table_undefended_frontpage_folds.sql b/roles/crawler/files/postgres-schemas/create_table_undefended_frontpage_folds.sql new file mode 100644 index 00000000..9e561dd8 --- /dev/null +++ b/roles/crawler/files/postgres-schemas/create_table_undefended_frontpage_folds.sql @@ -0,0 +1,29 @@ +CREATE TABLE models.undefended_frontpage_folds ( + foldid SERIAL PRIMARY KEY, + model_timestamp TIMESTAMP NOT NULL, + fold_timestamp TIMESTAMP NOT NULL, + auc NUMERIC NOT NULL, + tpr NUMERIC ARRAY, + fpr NUMERIC ARRAY, + precision_at_0_point_01_percent NUMERIC, + precision_at_0_point_05_percent NUMERIC, + precision_at_0_point_1_percent NUMERIC, + precision_at_0_point_5_percent NUMERIC, + precision_at_1_percent NUMERIC, + precision_at_5_percent NUMERIC, + precision_at_10_percent NUMERIC, + recall_at_0_point_01_percent NUMERIC, + recall_at_0_point_05_percent NUMERIC, + recall_at_0_point_1_percent NUMERIC, + recall_at_0_point_5_percent NUMERIC, + recall_at_1_percent NUMERIC, + recall_at_5_percent NUMERIC, + recall_at_10_percent NUMERIC, + f1_at_0_point_01_percent NUMERIC, + f1_at_0_point_05_percent NUMERIC, + f1_at_0_point_1_percent NUMERIC, + f1_at_0_point_5_percent NUMERIC, + f1_at_1_percent NUMERIC, + f1_at_5_percent NUMERIC, + f1_at_10_percent NUMERIC +); \ No newline at end of file diff --git a/roles/crawler/tasks/configure-databases.yml b/roles/crawler/tasks/configure-databases.yml index 26c8da1e..aee762c2 100644 --- a/roles/crawler/tasks/configure-databases.yml +++ b/roles/crawler/tasks/configure-databases.yml @@ -64,13 +64,14 @@ always_run: true changed_when: false - - name: Create the raw and features schemata. + - name: Create the raw, features, and models schemata. command: psql -c 'CREATE SCHEMA {{ item }};' when: "'{{ item }}' not in schemas.stdout" register: "schema_result" with_items: - raw - features + - models - name: List all tables in the raw schema. command: psql -c '\dt raw.*' @@ -78,7 +79,7 @@ always_run: true changed_when: false - - name: "Create the tables: crawls, hs_history, frontpage_examples, & frontpage_traces." + - name: "Create the tables: crawls, hs_history, and frontpage_examples tables." command: psql -c '{{ lookup("file", "postgres-schemas/"+item) }}' with_items: - create_table_crawls.sql @@ -90,6 +91,22 @@ when: item|basename|regex_replace('^create_table_(.*)\\.sql$', '\\1') not in tables.stdout register: raw_schema_tables_result + - name: List all tables in the models schema. + command: psql -c '\dt models.*' + register: models_tables + always_run: true + changed_when: false + + - name: "Create the tables: undefended_frontpage_folds and undefended_frontpage_attacks tables." + command: psql -c '{{ lookup("file", "postgres-schemas/"+item) }}' + with_items: + - create_table_undefended_frontpage_attacks.sql + - create_table_undefended_frontpage_folds.sql + # Each file is of the form create_table_.sql, so let's extract the + # expected table name and inspect the table list to check if it already exists. + when: item|basename|regex_replace('^create_table_(.*)\\.sql$', '\\1') not in models_tables.stdout + register: models_schema_tables_result + environment: "{{ fpsd_database_psql_env }}" # Configure test database @@ -116,6 +133,7 @@ when: "postgres_extension|changed or False in raw_schema_tables_result.results|map(attribute='skipped')|list + or False in models_schema_tables_result.results|map(attribute='skipped')|list or False in schema_result.results|map(attribute='skipped')|list" become: true become_user: postgres diff --git a/roles/database/files/create_schema_models.sql b/roles/database/files/create_schema_models.sql deleted file mode 100644 index f6f7adb7..00000000 --- a/roles/database/files/create_schema_models.sql +++ /dev/null @@ -1,2 +0,0 @@ -DROP SCHEMA IF EXISTS models CASCADE; -CREATE SCHEMA models; From 4977ac3dd09da3e59b34d360fcd5613df7066cf0 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Wed, 23 Nov 2016 15:32:37 -0800 Subject: [PATCH 22/27] Add new pip requirements from pip-compile.sh --- fpsd/requirements/requirements.txt | 62 +++++++++++++++++++++++++----- 1 file changed, 52 insertions(+), 10 deletions(-) diff --git a/fpsd/requirements/requirements.txt b/fpsd/requirements/requirements.txt index f9a24b86..1af5408f 100644 --- a/fpsd/requirements/requirements.txt +++ b/fpsd/requirements/requirements.txt @@ -4,21 +4,63 @@ # # pip-compile --output-file requirements.txt requirements.in # -aiohttp==1.0.5 +aiohttp==1.1.5 aiosocks==0.1.7 -async-timeout==1.0.0 # via aiohttp +async-timeout==1.1.0 # via aiohttp chardet==2.3.0 # via aiohttp +cycler==0.10.0 # via matplotlib +decorator==4.0.10 # via ipython, traitlets EasyProcess==0.2.3 # via pyvirtualdisplay -multidict==2.1.2 # via aiohttp -numpy==1.11.2 # via pandas -pandas==0.19.0 +entrypoints==0.2.2 # via nbconvert +ipykernel==4.5.1 # via ipywidgets, jupyter, jupyter-console, notebook, qtconsole +ipython-genutils==0.1.0 # via nbformat, notebook, traitlets +ipython==5.1.0 # via ipykernel, ipywidgets, jupyter-console +ipywidgets==5.2.2 # via jupyter +jinja2==2.8 # via nbconvert, notebook +jsonschema==2.5.1 # via nbformat +jupyter-client==4.4.0 # via ipykernel, jupyter-console, notebook, qtconsole +jupyter-console==5.0.0 # via jupyter +jupyter-core==4.2.0 # via jupyter-client, nbconvert, nbformat, notebook, qtconsole +jupyter==1.0.0 +MarkupSafe==0.23 # via jinja2 +matplotlib==1.5.3 +mistune==0.7.3 # via nbconvert +multidict==2.1.2 # via aiohttp, yarl +nbconvert==4.2.0 # via jupyter, notebook +nbformat==4.1.0 # via nbconvert, notebook +notebook==4.2.3 # via jupyter, widgetsnbextension +numpy==1.11.2 +pandas==0.19.1 +pexpect==4.2.1 # via ipython +pickleshare==0.7.4 # via ipython +prompt-toolkit==1.0.9 # via ipython, jupyter-console psycopg2==2.6.2 -python-dateutil==2.5.3 # via pandas -pytz==2016.7 # via pandas +ptyprocess==0.5.1 # via pexpect, terminado +pygments==2.1.3 # via ipython, jupyter-console, nbconvert, qtconsole +pyparsing==2.1.10 # via matplotlib +python-dateutil==2.6.0 # via matplotlib, pandas +pytz==2016.7 # via matplotlib, pandas pyvirtualdisplay==0.2.1 +PyYAML==3.12 +pyzmq==16.0.1 # via jupyter-client +qtconsole==4.2.1 # via jupyter +scikit-learn==0.17.1 +scipy==0.18.1 +seaborn==0.7.1 selenium==2.53.6 # via tbselenium -six==1.10.0 # via python-dateutil -SQLAlchemy==1.1.2 +simplegeneric==0.8.1 # via ipython +six==1.10.0 # via cycler, prompt-toolkit, python-dateutil, traitlets +SQLAlchemy==1.1.4 stem==1.4.0 tbselenium==0.1 -tqdm==4.8.4 +terminado==0.6 # via notebook +tornado==4.4.2 # via ipykernel, notebook, terminado +tqdm==4.10.0 +traitlets==4.3.1 # via ipykernel, ipython, ipywidgets, jupyter-client, jupyter-core, nbconvert, nbformat, notebook, qtconsole +wcwidth==0.1.7 # via prompt-toolkit +widgetsnbextension==1.2.6 # via ipywidgets +yarl==0.7.0 # via aiohttp + +# The following packages are commented out because they are +# considered to be unsafe in a requirements file: +# setuptools # via ipython From ce1e0adb228cca2cff39a81d3a05154596ee50ff Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Mon, 28 Nov 2016 11:42:08 -0800 Subject: [PATCH 23/27] Point database base class to use the PGPASSFILE Database needs a password, so I'm not sure how this was working before? Either way, it's there now, and reads from the environmental variable $PGPASSFILE. --- fpsd/database.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/fpsd/database.py b/fpsd/database.py index 111edf2b..265f489d 100644 --- a/fpsd/database.py +++ b/fpsd/database.py @@ -33,8 +33,12 @@ def __init__(self, database_config=None): config = get_config() database_config = dict(config.items("test_database")) try: + with open(os.environ["PGPASSFILE"], "rb") as f: + content = f.read().decode("utf-8").replace("\n", "").split(":") + database_config["pgpass"] = content[-1] + self.engine = create_engine( - 'postgresql://{pguser}:@{pghost}:{pgport}/{pgdatabase}'.format( + 'postgresql://{pguser}:{pgpass}@{pghost}:{pgport}/{pgdatabase}'.format( **database_config)) except OperationalError as exc: panic("fingerprint-securedrop Postgres support relies on use of a " From fe28cfe32e76bb76d3a6dc4eb21089acb4aa5682 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Mon, 28 Nov 2016 12:25:14 -0800 Subject: [PATCH 24/27] Label column should always be is_sd --- fpsd/database.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fpsd/database.py b/fpsd/database.py index 265f489d..b4d4d84c 100644 --- a/fpsd/database.py +++ b/fpsd/database.py @@ -205,7 +205,7 @@ def load_world(self, world_type): 'inner join raw.frontpage_examples t2 ' 'on t1.exampleid = t2.exampleid ' 'inner join raw.hs_history t3 ' - 'on t3.hsid = t2.hsid').format(world_type) + 'on t3.hsid = t2.hsid').format(select_hs_urls) df = pd.read_sql(labeled_query, self.engine) return df From d0d8896ae98210b5c7181a00e0a91999e317fe53 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Mon, 28 Nov 2016 12:33:18 -0800 Subject: [PATCH 25/27] Database classes should use test keyword --- fpsd/attack.py | 2 +- fpsd/classify.py | 2 +- fpsd/features.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) mode change 100644 => 100755 fpsd/attack.py diff --git a/fpsd/attack.py b/fpsd/attack.py old mode 100644 new mode 100755 index 383733e3..efeae266 --- a/fpsd/attack.py +++ b/fpsd/attack.py @@ -20,7 +20,7 @@ def run(options): with open(options, 'r') as f: options = yaml.load(f) - db = database.DatasetLoader() + db = database.DatasetLoader(test=False) df = db.load_world(options["world"]["type"]) diff --git a/fpsd/classify.py b/fpsd/classify.py index b115cc01..ca9985d6 100644 --- a/fpsd/classify.py +++ b/fpsd/classify.py @@ -47,7 +47,7 @@ def __init__(self, model_timestamp, world, model_type, self.n_cores = n_cores self.k = k self.feature_scaling = feature_scaling - self.db = database.ModelStorage() + self.db = database.ModelStorage(test=False) def get_dict(self): """Get config options as a dict""" diff --git a/fpsd/features.py b/fpsd/features.py index 69da6071..b4c0c049 100644 --- a/fpsd/features.py +++ b/fpsd/features.py @@ -731,7 +731,7 @@ def create_master_feature_view(self, feature_table_names): def compute_wang_feature_set(): - db = FeatureStorage() + db = FeatureStorage(test=False) # Create master table to store list of examples that we have generated features for db.create_table_undefended_frontpage_links() From 4fc9d54c0ceeb2be3e432fb7b8e631198e26be43 Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Mon, 28 Nov 2016 12:33:57 -0800 Subject: [PATCH 26/27] Add test keyword in database class The existing codes were always using the test database, which is not want we want. The production or test database can be selected using this new test keyword. --- fpsd/database.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/fpsd/database.py b/fpsd/database.py index b4d4d84c..118cb7d0 100644 --- a/fpsd/database.py +++ b/fpsd/database.py @@ -24,14 +24,21 @@ class Database: 'pgport', & 'pgdatabase'. If not passed values will read from the [database] section of './config.ini'. + :param bool test: An optional parameter specifiying if the test or + production database should be used. Defaults to + true (to use thetest database). :raises: An :exc:OperationalError, when unable to initialize the database engine with the given database configuration. """ - def __init__(self, database_config=None): + def __init__(self, database_config=None, test=True): if not database_config: config = get_config() - database_config = dict(config.items("test_database")) + if test: + database_config = dict(config.items("test_database")) + else: + database_config = dict(config.items("database")) + try: with open(os.environ["PGPASSFILE"], "rb") as f: content = f.read().decode("utf-8").replace("\n", "").split(":") From 5a474343d0cce044835c069ff5a563255f72ad5e Mon Sep 17 00:00:00 2001 From: redshiftzero Date: Mon, 28 Nov 2016 13:58:43 -0800 Subject: [PATCH 27/27] Replace get_dict() with __dict__ --- fpsd/classify.py | 15 +++------------ 1 file changed, 3 insertions(+), 12 deletions(-) diff --git a/fpsd/classify.py b/fpsd/classify.py index ca9985d6..b7ee0a25 100644 --- a/fpsd/classify.py +++ b/fpsd/classify.py @@ -48,17 +48,8 @@ def __init__(self, model_timestamp, world, model_type, self.k = k self.feature_scaling = feature_scaling self.db = database.ModelStorage(test=False) - - def get_dict(self): - """Get config options as a dict""" - - return {"hyperparameters": self.hyperparameters, - "model_type": self.model_type, - "world_type": self.world_type, - "observed_world_fraction": self.frac_obs, - "numfolds": self.k, - "train_class_balance": 'DEFAULT', - "base_rate": 'DEFAULT'} + self.train_class_balance = 'DEFAULT' + self.base_rate = 'DEFAULT' def train_single_fold(self, x_train, y_train): """Trains a model and saves it as self.trained_model @@ -161,7 +152,7 @@ def train_eval_all_folds(self, x_val, y_val): avg_metrics = evaluation.get_average_metrics(metrics_all_folds) # Save results of experiment (model evaluation averaged over all # folds) into the database - self.db.save_full_model(avg_metrics, self.model_timestamp, self.get_dict()) + self.db.save_full_model(avg_metrics, self.model_timestamp, self.__dict__) def pickle_results(self, pkl_file, to_save): with open(pkl_file, 'wb') as f: