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Commit e8ba6b3a authored by nikolasph's avatar nikolasph
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Updates

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with 1177 additions and 15 deletions
Code/FeatExtraction/ExecFeatParaOpt.py
+ 4
− 3
View file @ e8ba6b3a
......@@ -147,12 +147,13 @@ cl_desc = df_feat_res.c_cl_desc.values
# Description of Feature
feat_desc = df_feat_res.a_feat_desc.values
store = True
fileName = dir + datetime.datetime.now().strftime("%Y_%m_%d") + "-" + "Feature_" + args.feature + "-Parameter_" + args.param
# Show Results for Calculation
ExportResults.showScoreTime(fileName + "-TotalTime.png", score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
ExportResults.showScoreTime(fileName + "-FeatExtTime.png", score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Feature Extraction Time\n [s]')
ExportResults.showScoreTime(fileName + "-ClassTime.png", score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Classification Time\n [s]')
ExportResults.showScoreTime(fileName + "-TotalTime.png", store, score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
ExportResults.showScoreTime(fileName + "-FeatExtTime.png", store, score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Feature Extraction Time\n [s]')
ExportResults.showScoreTime(fileName + "-ClassTime.png", store, score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Classification Time\n [s]')
......
Code/FeatExtraction/ExecPlot.py 0 → 100644
+ 57
− 0
View file @ e8ba6b3a
import pandas as pd
import numpy as np
import datetime
import argparse # for acommand line arguments
import os # to geth path of the running script
import ExportResults # Functions to render results
parser = argparse.ArgumentParser(
description='This methods permits to execute a multiclass classification with one single view. At this point the used classifier is a RandomForest. The GridSearch permits to vary the number of trees and CrossValidation with k-folds.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
args = parser.parse_args()
args.valueEnd = 5
args.valueStart =75
args.nCalcs = 8
args.feature = "HOG"
args.param = "HOG_Cluster"
df_feat_res = pd.DataFrame.from_csv(path="D:\\BitBucket\\multiview-machine-learning-omis\\Code\\FeatExtraction\\Results-FeatParaOpt\\2016_03_19-FeatParaOpt-HOG.csv", sep=';')
# Get data from result to show results in plot
# Total time for feature extraction and classification
tot_time = df_feat_res.b_feat_extr_time.values + df_feat_res.e_cl_time.values
tot_time = np.asarray(tot_time)
# Time for feature extraction
feat_time = df_feat_res.b_feat_extr_time.values
feat_time = np.asarray(feat_time)
# Time for classification
cl_time = df_feat_res.e_cl_time.values
cl_time = np.asarray(cl_time)
# Mean Score of all classes
score = df_feat_res.f_cl_score.values
score = np.asarray(score)
# Range on X-Axis
if(args.nCalcs>1):
step = float(args.valueEnd-args.valueStart)/float(args.nCalcs-1)
rangeX = np.around(np.array(range(0,args.nCalcs))*step) + args.valueStart
else:
rangeX = [args.valueStart]
rangeX = np.asarray(rangeX)
# Description of Classification
cl_desc = df_feat_res.c_cl_desc.values
# Description of Feature
feat_desc = df_feat_res.a_feat_desc.values
dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-FeatParaOpt/"
fileName = dir + datetime.datetime.now().strftime("%Y_%m_%d") + "-" + "Feature_" + args.feature + "-Parameter_" + args.param
store = False
# Show Results for Calculation
ExportResults.showScoreTime(fileName + "-TotalTime.png", store, score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
ExportResults.showScoreTime(fileName + "-FeatExtTime.png", store, score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Feature Extraction Time\n [s]')
ExportResults.showScoreTime(fileName + "-ClassTime.png", store, score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation', 'Precision', 'Classification Time\n [s]')
\ No newline at end of file
Code/FeatExtraction/ExportResults.py
+ 10
− 12
View file @ e8ba6b3a
......@@ -28,11 +28,11 @@ def exportPandasToCSV(pandasSorDF, dir, filename):
for i in range(1,20):
testFileName = filename + "-" + str(i) + ".csv"
if os.path.isfile(dir + testFileName)!=True:
pandasSorDF.to_csv(dir + testFileName, sep=',')
pandasSorDF.to_csv(dir + testFileName, sep=';')
break
else:
pandasSorDF.to_csv(file + ".csv", sep=',')
pandasSorDF.to_csv(file + ".csv", sep=';')
def exportNumpyToCSV(numpyArray, dir, filename, format):
......@@ -42,17 +42,17 @@ def exportNumpyToCSV(numpyArray, dir, filename, format):
for i in range(1,20):
testFileName = filename + "-" + str(i) + ".csv"
if os.path.isfile(dir + testFileName )!=True:
np.savetxt(dir + testFileName, numpyArray, delimiter=",", fmt=format)
np.savetxt(dir + testFileName, numpyArray, delimiter=";", fmt=format)
break
else:
np.savetxt(file + ".csv", numpyArray, delimiter=",", fmt=format)
np.savetxt(file + ".csv", numpyArray, delimiter=";", fmt=format)
#### Rendering of results
### Rendering of Score and Time
def showScoreTime(filename, resScore, resTime, rangeX, parameter, feat_desc, cl_desc, fig_desc, y_desc1, y_desc2):
def showScoreTime(filename, store, resScore, resTime, rangeX, parameter, feat_desc, cl_desc, fig_desc, y_desc1, y_desc2):
# Determine interpolated functions
f_score_interp = interp1d(rangeX, resScore, kind='quadratic')
f_time_interp = interp1d(rangeX, resTime, kind='quadratic')
......@@ -111,12 +111,10 @@ def showScoreTime(filename, resScore, resTime, rangeX, parameter, feat_desc, cl_
plt.title(fig_desc, fontsize=18)
if(store):
plt.savefig(filename)
# instead of saving - decomment plt.show()
# plt.show()
else:
plt.show()
### Result comparision per class
......
Code/FeatExtraction/Results-FeatParaOpt/2016_03_19-FeatParaOpt-HOG-1.csv 0 → 100644
+ 146
− 0
View file @ e8ba6b3a
;a_feat_desc;b_feat_extr_time;c_cl_desc;d_cl_res;e_cl_time;f_cl_score
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Code/FeatExtraction/Results-FeatParaOpt/2016_03_19-FeatParaOpt-HOG.csv 0 → 100644
+ 233
− 0
View file @ e8ba6b3a
;a_feat_desc;b_feat_extr_time;c_cl_desc;d_cl_res;e_cl_time;f_cl_score
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loss_func=None, n_jobs=100,
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Code/FeatExtraction/__init__.py 0 → 100644
+ 1
− 0
View file @ e8ba6b3a
# Init
\ No newline at end of file
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