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test_knn_color.py
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test_knn_color.py
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import pandas as pd
import numpy as np
import knn
from BaysianClassifier import BaysianClassifier
data = pd.read_csv("segmentation_test.csv")
test = data
df = data.set_index('LABEL')
d = len(df.columns)
def compare(a,b):
if(a == b): return 1
return 0
comp = np.vectorize(compare)
def test(df):
ammount = df.iloc[:,0].groupby('LABEL').count()
ammount.columns = ['count']
result = pd.DataFrame(columns=['knn_color1','knn_color3','knn_color5','knn_color7','knn_color9'])
index = 0
for t in range(30):
print("test:"+str(t))
df_shape = df.iloc[:, 0:9]
df_color = df.iloc[:, 9:20]
#print (df_shape)
#print (df_color)
group_shape = {}
group_color = {}
for i in ammount.index:
group_shape[i] = np.split(df_shape.loc[i], 10)
group_color[i] = np.split(df_color.loc[i], 10)
for j in range(10):
train_shape = pd.DataFrame(columns = df_shape.columns)
test_shape = pd.DataFrame(columns = df_shape.columns)
train_color = pd.DataFrame(columns = df_color.columns)
test_color = pd.DataFrame(columns = df_color.columns)
print("j:" +str(j))
for i in ammount.index:
temp = group_shape[i].pop(0)
test_shape = pd.concat([test_shape, temp])
train_shape = pd.concat([train_shape] + group_shape[i])
group_shape[i].append(temp)
temp = group_color[i].pop(0)
test_color = pd.concat([test_color, temp])
train_color = pd.concat([train_color] + group_color[i])
group_color[i].append(temp)
test_shape.index.name = "LABEL"
test_color.index.name = "LABEL"
train_shape.index.name = "LABEL"
train_color.index.name = "LABEL"
knn_shape_correct = knn.knn_dataframes(test_color, train_color, 1)
knn_color_correct = knn.knn_dataframes(test_color, train_color, 3)
baysian_shape_correct = knn.knn_dataframes(test_color, train_color, 5)
baysian_color_correct = knn.knn_dataframes(test_color, train_color, 7)
majority_correct = knn.knn_dataframes(test_color, train_color, 9)
result.loc[index] = [knn_shape_correct, knn_color_correct, baysian_shape_correct, baysian_color_correct, majority_correct]
index += 1;
print(result.mean()/210.0)
df = df.sample(frac=1)
result.mean().to_csv("knn_color.csv")
test(df)