def plot_umafall():
umafall = UMAFALL_Model()
p = 1
relevant_features = s.load_var(
"umafall_relevant_features_best_window{}relevant_features_{}.pkl".
format(slash, p))
y = s.load_var("umafall_relevant_features_best_window{}y_{}.pkl".format(
slash, p))
y = pd.DataFrame(y, columns=[umafall.label_tag])
balanced_data = balance_data.balance_data(relevant_features, y,
threshold_balance_data)
#plot_hist(relevant_features, y, 'Base HMP desbalanceada.')
plot_hist(balanced_data[0], balanced_data[1], 'Base HMP Balanceada.')
def umafall():
umafall = UMAFALL_Model()
p = 1
umafall_threshold_classification = 0.45
relevant_features = s.load_var(
"umafall_relevant_features_best_window{}relevant_features_{}.pkl".
format(slash, p))
y = s.load_var("umafall_relevant_features_best_window{}y_{}.pkl".format(
slash, p))
y = pd.DataFrame(y, columns=[umafall.label_tag])
balanced_data = balance_data.balance_data(relevant_features, y,
threshold_balance_data)
plot_confusion_matrix(umafall, balanced_data[0], balanced_data[1],
umafall_threshold_classification)
from sklearn.ensemble import RandomForestClassifier # Random Forest
from sklearn.ensemble import ExtraTreesClassifier # Extra Trees
from sklearn.naive_bayes import GaussianNB #Naive Bayes
from sklearn import svm #SVM
from sklearn.neural_network import MLPClassifier #multi-layer percept
import pandas as pd
from sklearn.model_selection import train_test_split
from pre_processing.get_accuracy import Get_Accuracy
from scripts.save_workspace import save
import numpy as np
from pre_processing.balance_data import BalanceData
import statistics as st
#===INITIALIZATION===#
Debug.DEBUG = 0
umafall = UMAFALL_Model()
processing = Processing_DB_Files()
project = Project()
#tuple from MPL
t_aux = []
for i in range(0, 500):
t_aux.append(500)
t = tuple(t_aux)
####
classifiers = {
"MPL":
MLPClassifier(random_state=1,
solver="adam",
activation="relu",
max_iter=100000,
alpha=1e-5,
#===INIT BASES===#
hmp_persons = ["f1", "m1", "m2", "f2", "m3", "f3", "m4",
"f4"] # at least 5 activities
umafall_persons = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
arcma_persons = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
models = []
#Round 01
models.append({
"model_name": "hmp",
"model": HMP_Model(),
"persons": hmp_persons,
"window": 16
})
models.append({
"model_name": "umafall",
"model": UMAFALL_Model(),
"persons": umafall_persons,
"window": 10
})
models.append({
"model_name": "arcma",
"model": ARCMA_Model(),
"persons": arcma_persons,
"window": 26
})
#Round 02
#models.append({"model_name":"hmp", "model":HMP_Model(), "persons":hmp_persons, "window":90})
#models.append({"model_name":"umafall", "model":UMAFALL_Model(), "persons":umafall_persons, "window":10})
#models.append({"model_name":"arcma", "model":ARCMA_Model(), "persons":arcma_persons, "window":40})
#tuple from MPL
from models.arcma_model import ARCMA_Model
#===INITIALIZATION===#
Debug.DEBUG = 0
processing = Processing_DB_Files()
project = Project()
s = save()
get_accuracy = Get_Accuracy()
#===INIT BASES===#
hmp_persons = ["f1", "m1", "m2", "f2", "m3", "f3", "m4", "f4"] # at least 5 activities
umafall_persons = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]
arcma_persons = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
models = []
#models.append({"model_name":"hmp", "model":HMP_Model(), "persons":hmp_persons, "window":90})
models.append({"model_name":"umafall", "model":UMAFALL_Model(), "persons":umafall_persons, "window":10})
models.append({"model_name":"arcma", "model":ARCMA_Model(), "persons":arcma_persons, "window":40})
#tuple from MPL
t_aux = []
for i in range(0,500):
t_aux.append(500)
t = tuple(t_aux)
####
classifiers = {"Extratrees": ExtraTreesClassifier(n_estimators = 1000, random_state=1)}
#============OLD=============#