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real_experiment.py
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real_experiment.py
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#!/usr/bin/env python
import numpy as np
import pandas as pd
from siscm import SISCM
from helper import *
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB, CategoricalNB
np.random.seed(312253)
np.set_printoptions(precision=3)
class RealExperiment:
#class constructor
def __init__(self, n_classes, n_experts, seed=44):
self.rng = np.random.default_rng(seed)
self.n_classes = n_classes
self.n_experts = n_experts
self.proba_models = []
self.nb_baseline = []
self.list_marginal_proba_func = []
self.siscm_H = None
self.siscm_Psi = None
#add experts to the experiment by learning their marginal distribution models with the given data
def add_experts(self, expert_list, data, labels):
new_proba_func_list = self.fit_proba_models(expert_list, data, labels)
self.list_marginal_proba_func.extend(new_proba_func_list)
self.n_experts = len(self.list_marginal_proba_func)
#returns marginal probabilities of an expert's prediction for given features x
#Note, if class observations were missing during training, this function returns a very small probability for these classes,
def get_proba(self, x, clf):
classes_not_trained = set(clf.classes_).symmetric_difference(range(self.n_classes))
#print(classes_not_trained)
proba = clf.predict_proba(np.expand_dims(x, axis=0))
#print(proba)
if len(classes_not_trained)>0:
new_proba = np.empty((self.n_classes))
new_proba[list(classes_not_trained)] = 0.00000001
new_proba[clf.classes_] = proba[0]
new_proba[new_proba==0.0] = 0.00000001
return new_proba
proba = np.squeeze(proba, axis=0)
return np.where( proba > 0.0, proba, 0.00000001)
#fit marginal distribution models, GNB, for each expert
#returns array of distribution functions
def fit_proba_models(self,expert_list, data, labels):
for expert in expert_list:
X_train = data[labels[:, expert]!=-999]
y_train = labels[labels[:,expert]!=-999][:,expert]
gnb = GaussianNB()
gnb.fit(X_train, y_train)
self.proba_models.append(gnb)
return [(lambda x, clf=clf: self.get_proba(x, clf)) for clf in self.proba_models]
#create and fit SISCM model M(Psi) and create M(H) model
def update_model(self, data_train, labels_train):
#create and fit SI-SCM M(Psi)
if self.siscm_Psi == None:
self.siscm_Psi = SISCM("SISCM_M(Psi)", self.n_classes, self.list_marginal_proba_func)
self.siscm_Psi.fit( data_train, labels_train, val_ratio=0.0, max_rounds=10)
#create SI-SCM M(H)
if self.siscm_H == None:
self.siscm_H = SISCM("SISCM_M(H)", self.n_classes, self.list_marginal_proba_func, siscm_H= True)
#Evaluates the models estimating the marginal distibution of each expert
#Prints mean accuracy per expert and in total
def evaluate_marginal_estimators(self, data, labels):
scores = np.zeros((self.n_experts))
#test_predict = np.zeros((self.n_experts))
for expert, clf in enumerate(self.proba_models):
has_label=labels[:,expert]!=-999
scores[expert] = clf.score(data[has_label], labels[has_label][:,expert])
#test_predict[expert] = clf.predict(data[1].reshape(1, -1))[0]
print("mean accuracy of marginal estimators")
print(scores)
print(np.mean(scores))
#print(test_predict)
#evaluate SI-SCM model on data
def evaluate_siscm(self, data, labels, model_name="SISCM_M(Psi)"):
#pick correct SI-SCM
model = self.siscm_Psi
if model_name=="SISCM_M(H)":
model = self.siscm_H
print("Evaluating Gumbel-Max SI-SCM "+ model_name)
#counterfactual inference of an expert's prediction using another expert's observed prediction
n_labels_per_row = np.sum(labels!=-999, axis=1)
total_predictions = np.sum( n_labels_per_row * (n_labels_per_row-1))
eval_matrix = np.zeros((total_predictions, 17))
current_ind = 0
for obs_exp in range(self.n_experts):
obs_group = self.siscm_Psi.get_group_index(obs_exp)
has_data = labels[:,obs_exp]!=-999
proba = model.predict_cfc_proba(data[has_data], np.repeat(obs_exp,np.sum(has_data)), labels[has_data][:,obs_exp])
predictions = np.argmax(proba, axis=2)
data_indices = np.arange(data.shape[0], dtype=int)[has_data]
for i, x in enumerate(data_indices):
has_labels = labels[x]!=-999
has_labels[obs_exp]= False
exp_indices = np.arange(self.n_experts, dtype=int)[has_labels]
for exp in exp_indices:
same_group = self.siscm_Psi.get_group_index(exp) == obs_group
#set evaluation matrix entry with meta data and counterfactual inference result
eval_matrix[current_ind] = np.array([x,obs_exp,exp,labels[x,obs_exp], labels[x,exp], predictions[i,exp], same_group ]+ proba[i,exp].tolist(), dtype=float)
current_ind +=1
#save evaluation results
df_eval_matrix = pd.DataFrame(eval_matrix, columns = ["data_index", "obs_expert", "pred_expert", "obs_label","expert_label", "prediction", "is_same_group"] + ['proba_' + str(i) for i in range(10)])
df_eval_matrix.to_csv("results_real/evaluation_results_"+model_name+".csv", index=False)
#print overall mean
print(model_name)
print("Accuracy : ",np.mean(eval_matrix[:,4]==eval_matrix[:,5]))
#create and fit GNB+CNB baseline
def fit_nb_baseline(self, expert_list, data, labels):
for expert in expert_list:
X_train = data[labels[:, expert]!=-999]
y_train = labels[labels[:,expert]!=-999]
n_labels_per_row = np.sum(y_train!=-999, axis=1)-1
total_training_points = np.sum( n_labels_per_row )
img_features = np.zeros((total_training_points, X_train.shape[1]), dtype=float)
#one-hot encoding of observation
#zero-based: label 0 stands for unobserved, shifts all labels +1
#enc_X_train_obs = np.full((total_training_points, self.n_experts), 0, dtype=int)
#10-based: label 10 stands for unobserved
observed_pred = np.full((total_training_points, self.n_experts), 10, dtype=int)
new_y_train = np.zeros((total_training_points), dtype=int)
current_ind = 0
for obs_expert in range(self.n_experts):
if obs_expert != expert:
has_data = y_train[:,obs_expert]!=-999
for i,x in enumerate(X_train[has_data]):
enc = np.full(self.n_experts,10)
#enc = np.full(self.n_experts,0)
enc[obs_expert] = y_train[has_data][i,obs_expert]#+1
img_features[current_ind] = x
observed_pred[current_ind] = enc.astype(int)
new_y_train[current_ind] = y_train[has_data][i,expert]
current_ind +=1
X_train_gnb = img_features
X_train_cnb = observed_pred
y_train = new_y_train
#fit CNB given observed predictions
catnb = CategoricalNB(min_categories=np.repeat(11,self.n_experts))
catnb.fit(X_train_cnb, y_train)
#Uncomment to train and use new GNB with the images features
#gnb = GaussianNB()
#gnb.fit(X_train_gnb, y_train)
#self.nb_baseline.append((gnb,catnb))
#Saving the previously learned, marginal distribution GNB models and CNB as baseline
self.nb_baseline.append((self.proba_models[expert],catnb))
#evaluate GNB+CNB baseline model on data
def evaluate_nb_baseline(self, data, labels):
print("Evaluating GNB+CNB Baseline")
n_labels_per_row = np.sum(labels!=-999, axis=1)
total_predictions = np.sum( n_labels_per_row * (n_labels_per_row-1))
eval_matrix = np.zeros((total_predictions, 7))
#print(eval_matrix.shape)
current_ind = 0
for expert in range(self.n_experts):
expert_group = self.siscm_Psi.get_group_index(expert)
has_data = labels[:,expert]!=-999
gnb, catnb = self.nb_baseline[expert]
data_indices = np.arange(data.shape[0], dtype=int)[has_data]
for x in data_indices:
has_labels = labels[x]!=-999
has_labels[expert] = False
obs_indices = np.arange(self.n_experts, dtype=int)[has_labels]
for obs_expert in obs_indices:
same_group = self.siscm_Psi.get_group_index(obs_expert) == expert_group
has_data = labels[:,obs_expert]!=-999
#feat_enc = np.full(self.n_experts, 0)
feat_enc = np.full(self.n_experts, 10)
feat_enc[obs_expert] = labels[x,obs_expert]#+1
X_gnb = np.expand_dims(data[x],axis=0)
X_catnb = np.expand_dims(feat_enc,axis=0)
#combine marginal probabilities of both NB models
proba = gnb.predict_proba(X_gnb) * catnb.predict_proba(X_catnb) / gnb.class_prior_
#predict likeliest label
prediction = np.argmax(proba)
#set evaluation matrix entry with meta data and baseline prediction
eval_matrix[current_ind] = np.array([x, obs_expert, expert, labels[x,obs_expert], labels[x,expert], prediction, same_group ], dtype = int)
current_ind +=1
#save evaluation results
df_eval_matrix = pd.DataFrame(eval_matrix, columns = ["data_index", "obs_expert", "pred_expert", "obs_label","expert_label", "prediction", "is_same_group"])
df_eval_matrix.to_csv("results_real/evaluation_results_nb_baseline.csv", index=False)
#print overall mean
print("Accuracy : ", np.mean(eval_matrix[:,4]==eval_matrix[:,5]))
#evaluate GNB model on data
def evaluate_proba_models(self, data, labels):
print("Evaluating GNB marginal probability models")
n_labels_per_row = np.sum(labels!=-999, axis=1)
total_predictions = np.sum( n_labels_per_row)
eval_matrix = np.zeros((total_predictions, 7))
#print(eval_matrix.shape)
current_ind = 0
for expert in range(self.n_experts):
has_data = labels[:,expert]!=-999
data_indices = np.arange(data.shape[0], dtype=int)[has_data]
predictions = self.proba_models[expert].predict(data[has_data])
for i, x in enumerate(data_indices):
#set evaluation matrix entry with meta data and prediction
eval_matrix[current_ind] = np.array([x,-1,expert,-1, labels[x,expert], predictions[i], -1 ], dtype = int)
current_ind +=1
#save evaluation results
df_eval_matrix = pd.DataFrame(eval_matrix, columns = ["data_index", "obs_expert", "pred_expert", "obs_label","expert_label", "prediction", "is_same_group"])
df_eval_matrix.to_csv("results_real/evaluation_results_proba_models.csv", index=False)
#print overall mean
print("Accuracy : ",np.mean(eval_matrix[:,4]==eval_matrix[:,5]))
#save set Psi of expert groups found by fit algorithm to file
def save_groups(self):
groups = { i: g for i,g in enumerate(self.siscm_Psi.group_members_sorted)}
df_groups = pd.DataFrame.from_dict(groups, orient='index', dtype=int)
df_groups.to_csv('results_real/SI-SCM_groups.csv',index=False, header=False)
def main():
n_classes = 10
seed = 44
#read data
data = pd.read_csv('data/data_training.csv').to_numpy()
data_test = pd.read_csv('data/data_test.csv').to_numpy()
labels = pd.read_csv('data/labels_training.csv').to_numpy(dtype = 'int')
labels_test = pd.read_csv('data/labels_test.csv').to_numpy(dtype = 'int')
#print data size
print("Training data shape: ", data.shape)
print("Labels shape: ", labels.shape)
#number of experts in data
n_experts = labels.shape[1]
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
#preprocess the image features using PCA and scalers
num_features=20
pca = PCA(n_components=num_features)
sc = StandardScaler()
data = sc.fit_transform(data)
data = pca.fit_transform(data)
data_test = sc.transform(data_test)
data_test = pca.transform(data_test)
scaler = MinMaxScaler()
data = scaler.fit_transform(data)
data_test = scaler.transform(data_test)
#create experiment setup
exp = RealExperiment(n_classes, n_experts, seed)
exp.add_experts(range(n_experts), data, labels)
#exp.evaluate_marginal_estimators(data_test, labels_test)
exp.update_model(data, labels)
exp.save_groups()
#evaluate models on the test data
print("Test data shape: ", data_test.shape)
exp.evaluate_siscm( data_test, labels_test)
exp.evaluate_siscm( data_test, labels_test, model_name="SISCM_M(H)")
exp.evaluate_proba_models( data_test, labels_test)
#fit and evaluate baseline
exp.fit_nb_baseline( range(n_experts),data, labels)
exp.evaluate_nb_baseline( data_test, labels_test)
if __name__ == "__main__":
main()