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experiment_essentials.py
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experiment_essentials.py
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import numpy as np
import time
import pickle
import Orange
import tensorflow as tf
import pandas as pd
import arff
from rdm.db import CSVConnection,DBVendor, DBConnection, DBContext, RSDConverter, mapper, AlephConverter,TreeLikerConverter,OrangeConverter
from rdm.validation import cv_split
from rdm.wrappers import RSD
from rdm.wrappers import Aleph
from rdm.wrappers import TreeLiker
from rdm.wrappers import Wordification
from sklearn import preprocessing
from n_relaggs import batch_generator,nrelaggs_model,relaggs_generator,relaggs_model,seq_generator,context_converter
from sklearn.model_selection import StratifiedKFold
from joblib import Parallel, delayed
def transform(algorithm,context,target_att_value,seed,result_file,transformations,fold_nums=10):
fold_num = 0
for train_context, test_context in cv_split(context, folds=fold_nums, random_seed=seed):
fold_num += 1
print("FOLD",fold_num)
with open(result_file, 'a') as f:
f.write("FOLD {}\n".format(fold_num))
#ALEPH
if algorithm == "aleph":
start = time.time()
conv = AlephConverter(train_context, target_att_val=target_att_value)
aleph = Aleph()
train_arff, features = aleph.induce('induce_features',conv.positive_examples(),
conv.negative_examples(),
conv.background_knowledge(),printOutput=False)
data = arff.loads(str(train_arff))
entries = []
targets = []
for entry in data['data']:
en = list(entry)
features_target = en[-1]
features_train = en[0:len(en)-1]
features_train = [1 if x == "+" else 0 for x in features_train]
entries.append(features_train)
targets.append(features_target)
tmp_learner = 'aleph'
test_arff = mapper.domain_map(features, tmp_learner, train_context, test_context,format="csv",positive_class=target_att_value)
test_ins = test_arff.split("\n")
entries_test = []
targets_test = []
for entry in test_ins:
en = entry.strip().split(",")
if en[-1] != '':
features_target = en[-1]
features_train = en[0:len(en)-1]
features_train = [1 if x == "+" else 0 for x in features_train]
entries_test.append(features_train)
targets_test.append(features_target)
targets_test = ['positive' if x == target_att_value else 'negative' for x in targets_test]
train_features = pd.DataFrame(entries).to_numpy()
train_targets = pd.DataFrame(targets).to_numpy()
test_features = pd.DataFrame(entries_test).to_numpy()
test_targets = pd.DataFrame(targets_test).to_numpy()
le = preprocessing.LabelEncoder()
le.fit(train_targets)
targets_train_encoded = le.transform(train_targets)
targets_test_encoded = le.transform(test_targets)
end = time.time()
run_time = end - start
train_data = (train_features,targets_train_encoded)
test_data = (test_features,targets_test_encoded)
pickle.dump(train_data,open("{}_{}_train.p".format(transformations,fold_num),"wb"))
pickle.dump(test_data,open("{}_{}_test.p".format(transformations,fold_num),"wb"))
print(algorithm," TIME:",run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm,run_time))
#RSD
elif algorithm == "rsd":
start = time.time()
conv = RSDConverter(train_context)
rsd = RSD()
features, train_arff, _ = rsd.induce(conv.background_knowledge(),
examples=conv.all_examples(),cn2sd=False)
data = arff.loads(str(train_arff))
entries = []
targets = []
for entry in data['data']:
en = list(entry)
features_target = en[-1]
features_train = en[0:len(en)-1]
features_train = [1 if x == "+" else 0 for x in features_train]
entries.append(features_train)
targets.append(features_target)
tmp_learner = 'rsd'
test_arff = mapper.domain_map(features, tmp_learner, train_context, test_context,format="csv")
test_ins = test_arff.split("\n")
entries_test = []
targets_test = []
for entry in test_ins:
en = entry.strip().split(",")
if en[-1] != '':
features_target = en[-1]
features_train = en[0:len(en)-1]
features_train = [1 if x == "+" else 0 for x in features_train]
entries_test.append(features_train)
targets_test.append(features_target)
train_features = pd.DataFrame(entries).to_numpy()
train_targets = pd.DataFrame(targets).to_numpy()
test_features = pd.DataFrame(entries_test).to_numpy()
test_targets = pd.DataFrame(targets_test).to_numpy()
le = preprocessing.LabelEncoder()
le.fit(train_targets)
targets_train_encoded = le.transform(train_targets)
targets_test_encoded = le.transform(test_targets)
end = time.time()
run_time = end - start
train_data = (train_features,targets_train_encoded)
test_data = (test_features,targets_test_encoded)
pickle.dump(train_data,open("{}_{}_train.p".format(transformations,fold_num),"wb"))
pickle.dump(test_data,open("{}_{}_test.p".format(transformations,fold_num),"wb"))
print(algorithm," TIME:",run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm,run_time))
#Treeliker
elif algorithm == "treeliker":
start = time.time()
conv = TreeLikerConverter(train_context)
conv2 = TreeLikerConverter(test_context)
treeliker = TreeLiker(conv.dataset(), conv.default_template(),conv2.dataset())
train_arff, test_arff = treeliker.run()
wtag=False
entries = []
targets = []
entries_test = []
targets_test = []
for entry in train_arff.split("\n"):
if wtag:
en = entry.split(",")
if len(en)>1:
en = [x.replace(" ","") for x in en]
targets.append(en[-1])
en = [1 if "+" in x else 0 for x in en]
entries.append(en[0:len(en)-1])
if "@data" in entry:
wtag=True
wtag=False
for entry in test_arff.split("\n"):
if wtag:
en = entry.split(",")
if len(en) > 1:
en = [x.replace(" ","") for x in en]
targets_test.append(en[-1])
en = [1 if "+" in x else 0 for x in en]
entries_test.append(en[0:len(en)-1])
if "@data" in entry:
wtag=True
train_features = pd.DataFrame(entries).to_numpy()
train_targets = pd.DataFrame(targets).to_numpy()
test_features = pd.DataFrame(entries_test).to_numpy()
test_targets = pd.DataFrame(targets_test).to_numpy()
le = preprocessing.LabelEncoder()
le.fit(train_targets)
targets_train_encoded = le.transform(train_targets)
targets_test_encoded = le.transform(test_targets)
end = time.time()
run_time = end - start
train_data = (train_features,targets_train_encoded)
test_data = (test_features,targets_test_encoded)
pickle.dump(train_data,open("{}_{}_train.p".format(transformations,fold_num),"wb"))
pickle.dump(test_data,open("{}_{}_test.p".format(transformations,fold_num),"wb"))
print(algorithm," TIME:",run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm,run_time))
#Wordification
elif algorithm == "wordification":
start = time.time()
corange = OrangeConverter(train_context)
torange = OrangeConverter(test_context)
wordification = Wordification(corange.target_Orange_table(), corange.other_Orange_tables(), train_context)
wordification.run(1)
wordification.calculate_weights()
train_arff = wordification.to_arff()
wordification_test = Wordification(torange.target_Orange_table(), torange.other_Orange_tables(), test_context)
wordification_test.run(1)
wordification_test.calculate_weights()
idfs = wordification.idf
docs = wordification_test.resulting_documents
classes = [str(a) for a in wordification_test.resulting_classes]
feature_names = wordification.word_features
feature_vectors = []
for doc in docs:
doc_vec = []
for feature in feature_names:
cnt = 0
for x in doc:
if x == feature:
cnt+=1
idf = cnt * idfs[feature]
doc_vec.append(idf)
feature_vectors.append(doc_vec)
print(feature_vectors,classes)
test_arff = wordification_test.to_arff()
entries = []
targets = []
entries_test = []
targets_test = []
wtag = False
for entry in train_arff.split("\n"):
if wtag:
en = entry.split(",")
if len(en)>1:
en = [x.replace(" ","") for x in en]
targets.append(en[-1])
entries.append([float(x) for x in en[0:len(en)-1]])
if "@DATA" in entry:
wtag=True
wtag=False
targets_test = classes
entries_test = feature_vectors
train_features = pd.DataFrame(entries).to_numpy()
train_targets = pd.DataFrame(targets).to_numpy()
test_features = pd.DataFrame(entries_test).to_numpy()
test_targets = pd.DataFrame(targets_test).to_numpy()
le = preprocessing.LabelEncoder()
le.fit(np.concatenate([train_targets,test_targets]))
targets_train_encoded = le.transform(train_targets)
targets_test_encoded = le.transform(test_targets)
end = time.time()
run_time = end - start
train_data = (train_features,targets_train_encoded)
test_data = (test_features,targets_test_encoded)
pickle.dump(train_data,open("{}_{}_train.p".format(transformations,fold_num),"wb"))
pickle.dump(test_data,open("{}_{}_test.p".format(transformations,fold_num),"wb"))
print(algorithm," TIME:",run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm,run_time))
#relaggs/nrelaggs
else:
converter = context_converter(train_context, test_context, verbose=0)
train_data = converter.get_train()
test_data = converter.get_test()
plan = converter.get_plan()
pickle.dump(train_data,open("{}_{}_train.p".format(transformations,fold_num),"wb"))
pickle.dump(test_data,open("{}_{}_test.p".format(transformations,fold_num),"wb"))
pickle.dump(plan,open("{}_{}_plan.p".format(transformations,fold_num),"wb"))
run_time = converter.get_time()
print(algorithm," TIME:",run_time)
with open(result_file, 'a') as f:
f.write("{} TIME: {}\n".format(algorithm,run_time))
def experiment(algorithm,transformations,result_file,predictor_layers_,loss,feature_generation_,feature_selection_,fold_nums=10,epochs=100):
aurocs = []
accuracies = []
for fold_num in range(1,fold_nums+1):
train_data = pickle.load(open("{}_{}_train.p".format(transformations,fold_num),"rb"))
test_data = pickle.load(open("{}_{}_test.p".format(transformations,fold_num),"rb"))
if algorithm in ["relaggs","nrelaggs","nrelaggs_fix"]:
plan = pickle.load(open("{}_{}_plan.p".format(transformations,fold_num),"rb"))
if algorithm == "relaggs":
relaggs_data = relaggs_generator(train_data[0],plan)
X = relaggs_data.get_data()
y = train_data[1]
relaggs_data = relaggs_generator(test_data[0],plan)
X_test = relaggs_data.get_data()
y_test = test_data[1]
elif algorithm != "nrelaggs" and algorithm != "nrelaggs_fix":
X = train_data[0]
y = train_data[1][:,np.newaxis]
X_test = test_data[0]
y_test = test_data[1][:,np.newaxis]
if algorithm != "nrelaggs" and algorithm != "nrelaggs_fix":
best_params = [predictor_layers_[0]]
best_score = [0.]
#Parameter-Selection
for predictor_layers in predictor_layers_:
cur_score = 0.
skf = StratifiedKFold(n_splits=3)
#Fixing Multi-Class Case
if y.shape[-1] != 1:
y_split = np.where(y==1)[1]
else:
y_split = y
for train_index, test_index in skf.split(X,y_split):
X_train, X_val = X[train_index], X[test_index]
y_train, y_val = y[train_index], y[test_index]
train_batch = seq_generator(X_train,y_train)
val_batch = seq_generator(X_val,y_val)
rel_model = relaggs_model(train_batch.get_sizes(),predictor_layers,loss)
rel_model.train_model(train_batch,epochs)
acc,auroc = rel_model.evaluate_model(val_batch,y_val)
if y.shape[-1] != 1:
cur_score += auroc[0]
else:
cur_score += auroc
tf.keras.backend.clear_session()
if cur_score > best_score:
best_score = cur_score
best_params = [predictor_layers]
start = time.time()
train_batch = seq_generator(X,y)
test_batch = seq_generator(X_test,y_test)
rel_model = relaggs_model(train_batch.get_sizes(),best_params[0],loss)
rel_model.train_model(train_batch,epochs)
acc,auroc = rel_model.evaluate_model(test_batch,y_test)
tf.keras.backend.clear_session()
print("FOLD",fold_num)
print("BEST-PARAMS: ", best_params)
print("ACC:",acc," AUROC:",auroc)
with open(result_file, 'a') as f:
f.write("FOLD {}\n".format(fold_num))
f.write("BEST-PARAMS: {}\n".format(best_params))
f.write("ACC: {} AUROC: {}\n".format(acc,auroc))
end = time.time()
run_time = end - start
print(" TIME:",run_time)
with open(result_file, 'a') as f:
f.write("TIME: {}\n\n".format(run_time))
accuracies.append(acc)
aurocs.append(auroc)
if algorithm == "nrelaggs":
best_params = [predictor_layers_[0],feature_generation_[0],feature_selection_[0]]
best_score = [0.]
for predictor_layers in predictor_layers_:
for feature_generation in feature_generation_:
for feature_selection in feature_selection_:
cur_score = 0.
skf = StratifiedKFold(n_splits=3)
#Fixing Multi-Class Case
if train_data[1].shape[-1] != 1:
y_split = np.where(train_data[1]==1)[1]
else:
y_split = train_data[1]
for train_index, test_index in skf.split(y_split, y_split):
X_train = [train_data[0][i] for i in train_index]
X_val = [train_data[0][i] for i in test_index]
y_train, y_val = train_data[1][train_index], train_data[1][test_index]
train_batch = batch_generator(X_train,y_train)
val_batch = batch_generator(X_val,y_val)
rel_model = nrelaggs_model(train_batch.get_sizes(),plan,predictor_layers,
loss,feature_generation,feature_selection)
rel_model.train_model(train_batch,epochs)
acc,auroc = rel_model.evaluate_model(val_batch,y_val)
if train_data[1].shape[-1] != 1:
cur_score += auroc[0]
else:
cur_score += auroc
tf.keras.backend.clear_session()
if cur_score > best_score:
best_score = cur_score
best_params = [predictor_layers,feature_generation,feature_selection]
start = time.time()
train_batch = batch_generator(train_data[0],train_data[1])
test_batch = batch_generator(test_data[0],test_data[1])
rel_model = nrelaggs_model(train_batch.get_sizes(),plan,best_params[0],
loss,best_params[1],best_params[2])
rel_model.train_model(train_batch,epochs)
acc,auroc = rel_model.evaluate_model(test_batch,test_data[1])
tf.keras.backend.clear_session()
print("FOLD",fold_num)
print("BEST-PARAMS: ", best_params)
print("ACC:",acc," AUROC:",auroc)
with open(result_file, 'a') as f:
f.write("FOLD {}\n".format(fold_num))
f.write("BEST-PARAMS: {}\n".format(best_params))
f.write("ACC: {} AUROC: {}\n".format(acc,auroc))
end = time.time()
run_time = end - start
print(" TIME:",run_time)
with open(result_file, 'a') as f:
f.write("TIME: {}\n\n".format(run_time))
accuracies.append(acc)
aurocs.append(auroc)
if algorithm == "nrelaggs_fix":
best_params = [predictor_layers_[0]]
best_score = [0.]
for predictor_layers in predictor_layers_:
cur_score = 0.
skf = StratifiedKFold(n_splits=3)
#Fixing Multi-Class Case
if train_data[1].shape[-1] != 1:
y_split = np.where(train_data[1]==1)[1]
else:
y_split = train_data[1]
for train_index, test_index in skf.split(y_split, y_split):
X_train = [train_data[0][i] for i in train_index]
X_val = [train_data[0][i] for i in test_index]
y_train, y_val = train_data[1][train_index], train_data[1][test_index]
train_batch = batch_generator(X_train,y_train)
val_batch = batch_generator(X_val,y_val)
rel_model = nrelaggs_model(train_batch.get_sizes(),plan,predictor_layers,
loss,1.,1.)
rel_model.train_model(train_batch,epochs)
acc,auroc = rel_model.evaluate_model(val_batch,y_val)
if train_data[1].shape[-1] != 1:
cur_score += auroc[0]
else:
cur_score += auroc
tf.keras.backend.clear_session()
if cur_score > best_score:
best_score = cur_score
best_params = [predictor_layers]
start = time.time()
train_batch = batch_generator(train_data[0],train_data[1])
test_batch = batch_generator(test_data[0],test_data[1])
rel_model = nrelaggs_model(train_batch.get_sizes(),plan,best_params[0],
loss,1.,1.)
rel_model.train_model(train_batch,epochs)
acc,auroc = rel_model.evaluate_model(test_batch,test_data[1])
tf.keras.backend.clear_session()
print("FOLD",fold_num)
print("BEST-PARAMS: ", best_params)
print("ACC:",acc," AUROC:",auroc)
with open(result_file, 'a') as f:
f.write("FOLD {}\n".format(fold_num))
f.write("BEST-PARAMS: {}\n".format(best_params))
f.write("ACC: {} AUROC: {}\n".format(acc,auroc))
end = time.time()
run_time = end - start
print(" TIME:",run_time)
with open(result_file, 'a') as f:
f.write("TIME: {}\n\n".format(run_time))
accuracies.append(acc)
aurocs.append(auroc)
with open(result_file, 'a') as f:
f.write("ACCs\n")
f.write(",".join([str(a) for a in accuracies]))
f.write("\n AUROCS\n")
f.write(",".join([str(a) for a in aurocs]))
f.write("\n")