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Beginning model for training a spell recognizer

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This commit is contained in:
Lucas Oskorep
2020-03-14 17:52:56 -05:00
parent 21bcb96fea
commit fb70a81e87
5 changed files with 333 additions and 2 deletions
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.gitignore
+4 -1
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@@ -197,4 +197,7 @@ fabric.properties
# Android studio 3.1+ serialized cache file
.idea/caches/build_file_checksums.ser
data
data
train
test
val
collect_spell_data.py
+1 -1
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@@ -30,7 +30,7 @@ class DataCollectWand(Wand):
if pressed:
self.data = []
print("Button pressed")
await self.vibrate(PATTERN.BURST)
await self.vibrate(PATTERN.SHORT)
await self.reset_position()
await self.subscribe_position()
else:
spells.csv
+1
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@@ -3,6 +3,7 @@
"lumos"
"nox"
"accio"
"stupify"
"expelliarmus"
"engorgio"
"reducio"
1 spells
3 lumos
4 nox
5 accio
6 stupify
7 expelliarmus
8 engorgio
9 reducio
train-test-split.py
+96
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@@ -0,0 +1,96 @@
import os
from random import random
from shutil import copyfile, rmtree
import multiprocessing
train_dir = "./train/"
test_dir = "./test/"
val_dir = "./val/"
train = .80
test = .15
val = .05
def add_train_data(file, filename, label):
dest = train_dir + label + "/" + filename
if not os.path.exists(os.path.dirname(dest)):
try:
os.makedirs(os.path.dirname(dest))
except Exception as e:
print(e)
try:
copyfile(file, dest)
except Exception as e:
print(e)
print("INVALID FILE")
os.remove(file)
def add_val_data(file, filename, label):
dest = val_dir + label + "/" + filename
if not os.path.exists(os.path.dirname(dest)):
try:
os.makedirs(os.path.dirname(dest))
except Exception as e:
print(e)
copyfile(file, dest)
def add_test_data(file, filename, label):
dest = test_dir + label + "/" + filename
if not os.path.exists(os.path.dirname(dest)):
try:
os.makedirs(os.path.dirname(dest))
except Exception as e:
print(e)
copyfile(file, dest)
def remove_previous():
if os.path.exists(os.path.dirname(test_dir)):
rmtree(test_dir)
if os.path.exists(os.path.dirname(train_dir)):
rmtree(train_dir)
if os.path.exists(os.path.dirname(val_dir)):
rmtree(val_dir)
files_processed = 0
def test_split_file(file_root):
global files_processed
root = file_root[0]
file = file_root[1]
# print(file)
if file is ".DS_Store":
return
c = random()
if c < train:
add_train_data(os.path.join(root, file), file, root.split("/")[-1])
elif c < (train + val):
add_val_data(os.path.join(root, file), file, root.split("/")[-1])
else:
add_test_data(os.path.join(root, file), file, root.split("/")[-1])
files_processed += 1
if files_processed % 1000==0:
print(root.split("/")[-1])
print(files_processed)
print(file)
if __name__ == '__main__':
remove_previous()
file_root_list = []
for root, dirs, files in os.walk("data/"):
for file in files:
file_root_list.append((root, file))
pool = multiprocessing.Pool(multiprocessing.cpu_count()*2)
pool.map(test_split_file, file_root_list)
train_model.py
+231
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@@ -0,0 +1,231 @@
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt
from sklearn import svm
from pprint import pprint
import os
import seaborn as sn
def normalized(a, axis=-1, order=2):
return a / sum(a)
def get_activities(dir):
activity_data_train = {}
activity_data_test = {}
i= 0
for root, dirs, files in os.walk(dir):
activity_name = root.split('\\')[1]
activity_data_train[activity_name] = []
activity_data_test[activity_name] = []
for i in range(int(.8 * len(files))):
data = pd.read_csv(os.path.join(root, files[i]), delimiter=' ').values.flatten()
activity_data_train[activity_name].append(data)
i+=1
while i < len(files):
data = pd.read_csv(os.path.join(root, files[i]), delimiter=' ').values.flatten()
activity_data_test[activity_name].append(data)
i+=1
return activity_data_train, activity_data_test
def quantize_data(data, vector_len):
del data[""]
print("quantizing", data)
print(vector_len)
quantized_data = {}
total = 0
total2 = 0
for key, value in data.items():
print(key, len(value))
quantized_data[key] = {}
print(vector_len)
for i in range(len(value)):
total2+= len(value[i])/vector_len
while(len(value[i]) %vector_len != 0):
value[i] = value[i][:-1]
print(len(value[i]), vector_len)
new_data = np.split(value[i], len(value[i]) / vector_len)
# if len(new_data[len(new_data) - 1]) != vector_len:
# print(vector_len,len(new_data[len(new_data) - 1]))
# del new_data[len(new_data) - 1]
quantized_data[key][i] = new_data
total+= len(new_data)
print("Total is:", total, total2)
return quantized_data
lengths = [4]
# for i in range(3,4):
# lengths.append((i+1)*21)
data_train, data_test = get_activities('data\\')
print("----------------")
X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])
# print(X)
clusters =[1000]# [600,600,600,1000,1000,1000,1400,1400,1400]
q_count = 0
n_count = 0
accuracies = []
for quantize_length in lengths:
accuracies.append([])
for num_clusters in clusters:
qdata_test = quantize_data(data_test, quantize_length)
print("----------------")
qdata_train = quantize_data(data_train, quantize_length)
print("----------------")
#fill kmeans_data with all of the data of all of the exercises reguardless of what they are
k_means_data = None
for exercise_name, exercise_data in qdata_train.items():
for example_number, example_data in exercise_data.items():
if k_means_data is None:
k_means_data = np.array(example_data)
else:
# print(k_means_data.shape)
# print( np.atleast_1d(part).shape)
# print( np.atleast_2d(part).shape)
k_means_data = np.concatenate((k_means_data,np.array(example_data)), axis=0)
print(len(k_means_data[0]))
print(len(k_means_data[:,0]))
print("training the model")
kmeans = KMeans(n_clusters=num_clusters, n_init=15, max_iter= 500).fit(k_means_data)
print("Done training model")
centers = kmeans.cluster_centers_
#Time to run the training by again and make the histograms for each training data point.
classifier_training_X = []
classifier_training_Y = []
# print("creating histograms for training")
for exercise_name, exercise_data in qdata_train.items():
for example_number, example_data in exercise_data.items():
center_histo = np.zeros((num_clusters,))
center_indices = kmeans.predict(example_data)
for i in center_indices:
center_histo[i] += 1
classifier_training_X.append(normalized(center_histo))
classifier_training_Y.append(exercise_name)
# print("done")
classifier_test_X = []
classifier_test_Y = []
# print("creating histograms for testing")
for exercise_name, exercise_data in qdata_test.items():
for example_number, example_data in exercise_data.items():
center_histo = np.zeros((num_clusters,))
center_indices = kmeans.predict(example_data)
for i in center_indices:
center_histo[i] += 1
classifier_test_X.append(normalized(center_histo))
classifier_test_Y.append(exercise_name)
# print(classifier_test_Y)
print("done")
forest_average = []
for g in range(1):
clf = RandomForestClassifier(n_estimators=(50)*2)
clf.fit(classifier_training_X, classifier_training_Y)
results = clf.predict(classifier_test_X)
print(results)
i = 0
#
#
con_mat = confusion_matrix(results, classifier_test_Y)
print(con_mat)
np.savetxt("con_mat.csv", con_mat, '%5.0f', delimiter=",\t")
wrong = 0
right = 0
for i in range(len(con_mat)):
for j in range(len(con_mat[0])):
if i != j:
wrong += con_mat[i][j]
else:
right += con_mat[i][j]
# print(right/len(results))
# print((g+1)*2)
forest_average.append(right/len(results))
print("Random Forest Average Accuracy:")
print(sum(forest_average) / len(forest_average))
print("SVM Average Accuracy:")
clf = svm.LinearSVC()
clf.fit(classifier_training_X, classifier_training_Y)
results = clf.predict(classifier_test_X)
# print(results)
i = 0
con_mat = confusion_matrix(results, classifier_test_Y)
# print(con_mat)
wrong = 0
right = 0
for i in range(len(con_mat)):
for j in range(len(con_mat[0])):
if i != j:
wrong += con_mat[i][j]
else:
right += con_mat[i][j]
print(right/len(results))
print("Gaussian Naive Bayes Average Accuracy:")
clf = GaussianNB()
clf.fit(classifier_training_X, classifier_training_Y)
results = clf.predict(classifier_test_X)
# print(results)
i = 0
con_mat = confusion_matrix(results, classifier_test_Y)
# print(con_mat)
wrong = 0
right = 0
for i in range(len(con_mat)):
for j in range(len(con_mat[0])):
if i != j:
wrong += con_mat[i][j]
else:
right += con_mat[i][j]
print(right/len(results))
accuracies[q_count].append(sum(forest_average) / len(forest_average))
print(num_clusters)
print(quantize_length)
n_count+=1
n_count = 0
q_count +=1
#Following code copied froms tack overflow for printing out the confusion matrix
#convert the string into an array
df_cm = pd.DataFrame(con_mat, index = [i for i in "ABCDEFGHIJKLMN"],
columns = [i for i in "ABCDEFGHIJKLMN"])
plt.figure(figsize = (10,7))
sn.heatmap(df_cm, annot=True)
pprint(accuracies)
plt.show()