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Main.py
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Main.py
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import Utils
import data_utils as du
import re
import os
import numpy as np
from datetime import datetime
import json
import matplotlib.pyplot as plt
import SP_coTrain
from sklearn.svm import SVC
from sklearn.utils import shuffle
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import roc_auc_score, accuracy_score
CONFIG = "ITERS"
BASE_MODELS = {"SVC": SVC(probability=True),
"RandomForest": RandomForestClassifier(),
"NaiveBayes": GaussianNB()}
LABELED_RATIOS = [0.2, 0.4, 0.6]
ITERS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
ITERS_BASE_MODEL = "RandomForest"
def evaluate_model(base_line, base_model, X_labeled, X_unlabeled, y_labeled, X_test, y_test, view1, view2,
labeled_rate):
# get the model to evaluate
model = Utils.get_model(base_line, BASE_MODELS[base_model], X_unlabeled, labeled_rate)
# train the model
start = datetime.now()
if base_line == "reg_base":
model.fit(X_labeled, y_labeled)
else:
model.fit(X_labeled, X_unlabeled, y_labeled, view1, view2)
end = datetime.now()
fit_time = (end - start).total_seconds()
# predict
start = datetime.now()
y_pred = model.predict(X_test)
end = datetime.now()
predict_time = (end - start).total_seconds()
# calculate accuracy and auc
y_pred = np.array(y_pred)
auc = roc_auc_score(y_test, y_pred)
acc = accuracy_score(y_test, y_pred)
return fit_time, predict_time, auc, acc
# Evaluates the algorithm with 10 fold cv each time the train is shuffled and splitted randomly to train and test.
def cross_validation(dir_path, file_name, cv=10, base_model="RandomForest", labeled_rate=0.2):
X, y, view1, view2 = du.extract_data(dir_path + '/' + file_name, file_name)
res_spaco = []
res_co = []
res_base = []
res_reg_base = []
for i in range(cv):
print("CV: " + str(i))
X, y = shuffle(X, y)
X_labeled, X_unlabeled, y_labeled, X_test, y_test = du.split_data(X, y, train_test_split=0.8,
labeled_unlabeled_split=labeled_rate)
res_spaco.append(
evaluate_model("spaco", base_model, X_labeled, X_unlabeled, y_labeled, X_test, y_test, view1, view2,
labeled_rate))
res_co.append(evaluate_model("co", base_model, X_labeled, X_unlabeled, y_labeled, X_test, y_test, view1, view2,
labeled_rate))
res_base.append(
evaluate_model("base", base_model, X_labeled, X_unlabeled, y_labeled, X_test, y_test, view1, view2,
labeled_rate))
res_reg_base.append(
evaluate_model("reg_base", base_model, X_labeled, X_unlabeled, y_labeled, X_test, y_test, view1, view2,
labeled_rate))
return res_spaco, res_co, res_base, res_reg_base
# Runs the experiment on the given data
def run_experiment(dir_path, file_name):
res_spaco = {}
res_co = {}
res_base = {}
res_reg_base = {}
for base_model in BASE_MODELS.keys():
res_spaco[base_model] = {}
res_co[base_model] = {}
res_base[base_model] = {}
res_reg_base[base_model] = {}
for labled_ratio in LABELED_RATIOS:
res_spaco[base_model][str(labled_ratio)], res_co[base_model][str(labled_ratio)], res_base[base_model][
str(labled_ratio)], res_reg_base[base_model][str(labled_ratio)] = cross_validation(dir_path, file_name, 10,
base_model,
labled_ratio)
# Dump all the results
internal_dir_name = re.sub(r".csv", "", file_name)
f = open("./results/" + internal_dir_name + "/spaco.json", 'w')
json.dump(res_spaco, f)
f.close()
f = open("./results/" + internal_dir_name + "/co.json", 'w')
json.dump(res_co, f)
f.close()
f = open("./results/" + internal_dir_name + "/base.json", 'w')
json.dump(res_base, f)
f.close()
f = open("./results/" + internal_dir_name + "/reg_base.json", 'w')
json.dump(res_reg_base, f)
f.close()
directory_in_str = "./data"
directory = os.fsencode(directory_in_str)
def run_iters_experiment(dir_path, file_name):
res = {}
res['x']=[]
res['y']=[]
for num_of_iters in ITERS:
X, y, view1, view2 = du.extract_data(dir_path + '/' + file_name, file_name)
acc = []
for i in range(10):
print("CV: " + str(i))
X, y = shuffle(X, y)
X_labeled, X_unlabeled, y_labeled, X_test, y_test = du.split_data(X, y, train_test_split=0.8,
labeled_unlabeled_split=0.2)
base_models = [BASE_MODELS[ITERS_BASE_MODEL], BASE_MODELS[ITERS_BASE_MODEL]]
model = SP_coTrain.SP_coTrain(base_models, num_of_iters, add_rate=0.1, gamma=0.5)
model.fit(X_labeled, X_unlabeled, y_labeled, view1, view2)
y_pred = model.predict(X_test)
acc.append(accuracy_score(y_test, y_pred))
res['x'].append(num_of_iters)
res['y'].append(sum(acc) / len(acc))
plot_graph(res)
def plot_graph(results,title="Accuracy per Num OF Iterations"):
plt.title(title)
plt.xlabel("Num OF Iters")
plt.ylabel("Accuracy")
plt.plot(results['x'],results['y'])
plt.savefig("./charts/" + title + ".png")
plt.close()
if CONFIG != "ITERS":
# loop on all the files
for file in os.listdir(directory):
file_name = os.fsdecode(file)
run_experiment(directory_in_str, file_name)
else:
# loop on all the files
for file in os.listdir(directory):
file_name = os.fsdecode(file)
run_iters_experiment(directory_in_str, file_name)