From e544ff05bdce24c2e017081ad157cc652b35ddbe Mon Sep 17 00:00:00 2001
From: nikolasph <nikolas.huelsmann@gmail.com>
Date: Tue, 22 Mar 2016 16:42:54 +0100
Subject: [PATCH] Refactoring
---
Code/Niko/ExecClassifMonoView.py | 10 +-
Code/Niko/ExecFeatExtraction.py | 14 +--
Code/Niko/ExecFeatParaOpt.py | 36 +++---
Code/Niko/ExportResults.py | 27 +++--
Code/Niko/FeatExtraction.py | 8 +-
.../2016_03_22-CMV-CTMini-RGB-LOG.log | 16 +++
.../2016_03_22-CMV-CTMini-RGB.csv | 12 ++
.../2016_03_22-CMV-CTMini-RGB.png | Bin 0 -> 22705 bytes
...3_22-FE-CTMini-ClassLabels-Description.csv | 2 +
.../2016_03_22-FE-CTMini-ClassLabels.csv | 97 +++++++++++++++
...rientaions_8-nbClusters_12-Maxiter_100.csv | 97 +++++++++++++++
...-CTMini-HSV-Bins_[16, 4, 4]-Norm_Distr.csv | 97 +++++++++++++++
...TMini-RGB-Bins_16-MaxCI_256-Norm_Distr.csv | 97 +++++++++++++++
...22-FE-CTMini-RGB_HSV_SIFT_SURF_HOG-LOG.log | 41 +++++++
...2-FE-CTMini-SIFT-Cluster_50-Norm_Distr.csv | 97 +++++++++++++++
...2-FE-CTMini-SURF-Cluster_50-Norm_Distr.csv | 97 +++++++++++++++
...3_22-FPO-CTMini-RGB-RGB_Bins-ClassTime.png | Bin 0 -> 216557 bytes
...22-FPO-CTMini-RGB-RGB_Bins-FeatExtTime.png | Bin 0 -> 217605 bytes
...2016_03_22-FPO-CTMini-RGB-RGB_Bins-LOG.log | 50 ++++++++
...3_22-FPO-CTMini-RGB-RGB_Bins-TotalTime.png | Bin 0 -> 218899 bytes
.../2016_03_22-FPO-CTMini-RGB-RGB_Bins.csv | 111 ++++++++++++++++++
21 files changed, 867 insertions(+), 42 deletions(-)
create mode 100644 Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB-LOG.log
create mode 100644 Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB.csv
create mode 100644 Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB.png
create mode 100644 Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-ClassLabels-Description.csv
create mode 100644 Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-ClassLabels.csv
create mode 100644 Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-HOG-CellDimension_5-nbOrientaions_8-nbClusters_12-Maxiter_100.csv
create mode 100644 Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-HSV-Bins_[16, 4, 4]-Norm_Distr.csv
create mode 100644 Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-RGB-Bins_16-MaxCI_256-Norm_Distr.csv
create mode 100644 Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-RGB_HSV_SIFT_SURF_HOG-LOG.log
create mode 100644 Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-SIFT-Cluster_50-Norm_Distr.csv
create mode 100644 Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-SURF-Cluster_50-Norm_Distr.csv
create mode 100644 Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-ClassTime.png
create mode 100644 Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-FeatExtTime.png
create mode 100644 Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-LOG.log
create mode 100644 Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-TotalTime.png
create mode 100644 Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins.csv
diff --git a/Code/Niko/ExecClassifMonoView.py b/Code/Niko/ExecClassifMonoView.py
index 76f6d8db..1aee04ef 100644
--- a/Code/Niko/ExecClassifMonoView.py
+++ b/Code/Niko/ExecClassifMonoView.py
@@ -25,18 +25,18 @@ __date__ = 2016-03-25
### Argument Parser
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.',
+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. The result will be a plot of the score per class and a CSV with the best classifier found by the GridSearch.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-groupStandard = parser.add_argument_group('necessary arguments:')
+groupStandard = parser.add_argument_group('necessary arguments')
groupStandard.add_argument('--name', metavar='STRING', action='store', help='Name of Database (default: %(default)s)', default='Caltech')
groupStandard.add_argument('--feat', metavar='STRING', action='store', help='Name of Feature for Classification (default: %(default)s)', default='RGB')
-groupStandard.add_argument('--pathF', metavar='STRING', action='store', help='Path to the features (default: %(default)s)', default='Results-FeatExtr\\')
+groupStandard.add_argument('--pathF', metavar='STRING', action='store', help='Path to the features (default: %(default)s)', default='Results-FeatExtr/')
groupStandard.add_argument('--fileCL', metavar='STRING', action='store', help='Name of classLabels CSV-file (default: %(default)s)', default='classLabels.csv')
groupStandard.add_argument('--fileFeat', metavar='STRING', action='store', help='Name of feature CSV-file (default: %(default)s)', default='feature.csv')
groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
-groupClass = parser.add_argument_group('Classification arguments:')
+groupClass = parser.add_argument_group('Classification arguments')
groupClass.add_argument('--CL_split', metavar='FLOAT', action='store', help='Determine the the train size', type=float, default=0.8)
groupClass.add_argument('--CL_RF_trees', metavar='STRING', action='store', help='GridSearch: Determine the trees', default='50 100 150 200')
groupClass.add_argument('--CL_RF_CV', metavar='INT', action='store', help='Number of k-folds for CV', type=int, default=8)
@@ -50,7 +50,7 @@ t_start = time.time()
# Configure Logger
dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
-logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-LOG-CMV-" + args.name + "-" + args.feat
+logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + args.name + "-" + args.feat + "-LOG"
logfile = dir + logfilename
if os.path.isfile(logfile + ".log"):
for i in range(1,20):
diff --git a/Code/Niko/ExecFeatExtraction.py b/Code/Niko/ExecFeatExtraction.py
index 3412c8ca..37601ac2 100644
--- a/Code/Niko/ExecFeatExtraction.py
+++ b/Code/Niko/ExecFeatExtraction.py
@@ -27,35 +27,35 @@ parser = argparse.ArgumentParser(
description='This methods permits to export one or more features at the same time for a database of images (path, name). To extract one feature activate it by using the specific argument (e.g. -RGB). For each feature you can define the parameters by using the optional arguments (e.g. --RGB_Hist 32). The results will be exported to a CSV-File.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-groupStandard = parser.add_argument_group('necessary arguments:')
+groupStandard = parser.add_argument_group('necessary arguments')
groupStandard.add_argument('--name', metavar='STRING', action='store', help='Select a name of DB, e.g. Caltech (default: %(default)s)', default='DB')
groupStandard.add_argument('--path', metavar='STRING', action='store', help='Path to the database (default: %(default)s)', default='D:\\CaltechMini')
groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
-groupRGB = parser.add_argument_group('RGB arguments:')
+groupRGB = parser.add_argument_group('RGB arguments')
groupRGB.add_argument('-RGB', action='store_true', help='Use option to activate RGB')
groupRGB.add_argument('--RGB_Bins', metavar='INT', action='store', help='Number of bins for histogram', type=int, default=16)
groupRGB.add_argument('--RGB_CI', metavar='INT', action='store', help='Max Color Intensity [0 to VALUE]', type=int, default=256)
groupRGB.add_argument('-RGB_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
-groupHSV = parser.add_argument_group('HSV arguments:')
+groupHSV = parser.add_argument_group('HSV arguments')
groupHSV.add_argument('-HSV', action='store_true', help='Use option to activate HSV')
groupHSV.add_argument('--HSV_H_Bins', metavar='INT', action='store', help='Number of bins for Hue', type=int, default=16)
groupHSV.add_argument('--HSV_S_Bins', metavar='INT', action='store', help='Number of bins for Saturation', type=int, default=4)
groupHSV.add_argument('--HSV_V_Bins', metavar='INT', action='store', help='Number of bins for Value', type=int, default=4)
groupHSV.add_argument('-HSV_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
-groupSIFT = parser.add_argument_group('SIFT arguments:')
+groupSIFT = parser.add_argument_group('SIFT arguments')
groupSIFT.add_argument('-SIFT', action='store_true', help='Use option to activate SIFT')
groupSIFT.add_argument('--SIFT_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=50)
groupSIFT.add_argument('-SIFT_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
-groupSURF = parser.add_argument_group('SURF arguments:')
+groupSURF = parser.add_argument_group('SURF arguments')
groupSURF.add_argument('-SURF', action='store_true', help='Use option to activate SURF')
groupSURF.add_argument('--SURF_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=50)
groupSURF.add_argument('-SURF_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
-groupHOG = parser.add_argument_group('HOG arguments:')
+groupHOG = parser.add_argument_group('HOG arguments')
groupHOG.add_argument('-HOG', action='store_true', help='Use option to activate HOG')
groupHOG.add_argument('--HOG_CellD', metavar='INT', action='store', help='CellDimension for local histograms', type=int, default=5)
groupHOG.add_argument('--HOG_Orient', metavar='INT', action='store', help='Number of bins of local histograms', type=int, default=8)
@@ -93,7 +93,7 @@ if(args.HOG):
# Configure Logger
dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-FeatExtr/"
-logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-LOG-FE-" + args.name + "-" + features.replace(" ", "_").rstrip("_")
+logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-FE-" + args.name + "-" + features.replace(" ", "_").rstrip("_") + "-LOG"
logfile = dir + logfilename
if os.path.isfile(logfile + ".log"):
for i in range(1,20):
diff --git a/Code/Niko/ExecFeatParaOpt.py b/Code/Niko/ExecFeatParaOpt.py
index c3b4a527..f48c0079 100644
--- a/Code/Niko/ExecFeatParaOpt.py
+++ b/Code/Niko/ExecFeatParaOpt.py
@@ -27,45 +27,46 @@ parser = argparse.ArgumentParser(
description='This methods permits to perform an optimisation of the parameter of one feature. Therefore you have so specify which feature to use (e.g. --feature RGB) and which of his parameters (the parameters depend on the feature chosen, e.g. for RGB: --parameter Bins). The method will calculate the results in your given range and export the results to a CSV-File.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-groupStandard = parser.add_argument_group('necessary arguments:')
+groupStandard = parser.add_argument_group('necessary arguments')
groupStandard.add_argument('--name', metavar='STRING', action='store', help='Select a name of DB, e.g. Caltech (default: %(default)s)', default='DB')
groupStandard.add_argument('--path', metavar='STRING', action='store', help='Path to the database (default: %(default)s)', default='D:\\CaltechMini')
groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
+groupStandard.add_argument('-show', action='store_true', help='Use option to show results instead of saving it')
-groupOpt = parser.add_argument_group('Optimisation arguments:')
+groupOpt = parser.add_argument_group('Optimisation arguments')
groupOpt.add_argument('--feature', choices=['RGB', 'HSV', 'SURF', 'SIFT', 'HOG'], help='Set feature from list (RGB, HSV, ..)', default='RGB')
groupOpt.add_argument('--param', choices=['RGB_Bins', 'RGB_MaxCI', 'HSV_H_Bins', 'HSV_S_Bins', 'HSV_V_Bins', 'SIFT_Cluster', 'SURF_Cluster', 'HOG_Cluster'], help='Parameter to optimise (remember depends on feature)', default='RGB_Bins')
groupOpt.add_argument('--valueStart', metavar='INT', action='store', help='Start-Value for optimisation range', type=int)
groupOpt.add_argument('--valueEnd', metavar='INT', action='store', help='End-Value for optimisation range', type=int)
groupOpt.add_argument('--nCalcs', metavar='INT', action='store', help='Number of calculations between Start and End-Value', type=int)
-groupRGB = parser.add_argument_group('RGB arguments:')
+groupRGB = parser.add_argument_group('RGB arguments')
groupRGB.add_argument('--RGB_Bins', metavar='INT', action='store', help='Number of bins for histogram', type=int, default=16)
groupRGB.add_argument('--RGB_CI', metavar='INT', action='store', help='Max Color Intensity [0 to VALUE]', type=int, default=256)
groupRGB.add_argument('-RGB_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
-groupHSV = parser.add_argument_group('HSV arguments:')
+groupHSV = parser.add_argument_group('HSV arguments')
groupHSV.add_argument('--HSV_H_Bins', metavar='INT', action='store', help='Number of bins for Hue', type=int, default=16)
groupHSV.add_argument('--HSV_S_Bins', metavar='INT', action='store', help='Number of bins for Saturation', type=int, default=4)
groupHSV.add_argument('--HSV_V_Bins', metavar='INT', action='store', help='Number of bins for Value', type=int, default=4)
groupHSV.add_argument('-HSV_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
-groupSIFT = parser.add_argument_group('SIFT arguments:')
+groupSIFT = parser.add_argument_group('SIFT arguments')
groupSIFT.add_argument('--SIFT_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=50)
groupSIFT.add_argument('-SIFT_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
-groupSURF = parser.add_argument_group('SURF arguments:')
+groupSURF = parser.add_argument_group('SURF arguments')
groupSURF.add_argument('--SURF_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=50)
groupSURF.add_argument('-SURF_NMinMax', action='store_true', help='Use option to actvate MinMax Norm instead of Distribution')
-groupHOG = parser.add_argument_group('HOG arguments:')
+groupHOG = parser.add_argument_group('HOG arguments')
groupHOG.add_argument('--HOG_CellD', metavar='INT', action='store', help='CellDimension for local histograms', type=int, default=5)
groupHOG.add_argument('--HOG_Orient', metavar='INT', action='store', help='Number of bins of local histograms', type=int, default=8)
groupHOG.add_argument('--HOG_Cluster', metavar='INT', action='store', help='Number of k-means cluster', type=int, default=12)
groupHOG.add_argument('--HOG_Iter', metavar='INT', action='store', help='Max. number of iterations for clustering', type=int, default=100)
groupHOG.add_argument('--HOG_cores', metavar='INT', action='store', help='Number of cores for HOG', type=int, default=1)
-groupClass = parser.add_argument_group('Classification arguments:')
+groupClass = parser.add_argument_group('Classification arguments')
groupClass.add_argument('--CL_split', metavar='FLOAT', action='store', help='Determine the train size', type=float, default=0.8)
groupClass.add_argument('--CL_RF_trees', metavar='STRING', action='store', help='GridSearch: Determine the trees', default='50 100 150 200')
groupClass.add_argument('--CL_RF_CV', metavar='INT', action='store', help='Number of k-folds for CV', type=int, default=8)
@@ -92,7 +93,7 @@ para_Cl = [args.CL_split, map(int, args.CL_RF_trees.split()), args.CL_RF_CV, arg
# Configure Logger
dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-FeatParaOpt/"
-logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-LOG-FPO-" + args.name + "-" + args.feat + "-" + args.param
+logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-FPO-" + args.name + "-" + args.feature + "-" + args.param + "-LOG"
logfile = dir + logfilename
if os.path.isfile(logfile + ".log"):
for i in range(1,20):
@@ -134,7 +135,7 @@ logging.debug("### Done:\t Feautre Optimisation ")
################################ Render results
logging.debug("### Start:\t Exporting to CSV ")
dir = os.path.dirname(os.path.abspath(__file__)) + "/Results-FeatParaOpt/"
-filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-FeatParaOpt-" + args.feature
+filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-FPO-" + args.name + "-" + args.feature + "-" + args.param
ExportResults.exportPandasToCSV(df_feat_res, dir, filename)
logging.debug("### Done:\t Exporting to CSV ")
@@ -168,13 +169,18 @@ cl_desc = df_feat_res.c_cl_desc.values
# Description of Feature
feat_desc = df_feat_res.a_feat_desc.values
-store = True
-fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-" + "-FPO-" + args.name + "-" + args.feature + "-" + args.param
+# Store or Show plot
+if(args.show):
+ store = False
+else:
+ store = True
+
+fileName = datetime.datetime.now().strftime("%Y_%m_%d") + "-FPO-" + args.name + "-" + args.feature + "-" + args.param
# Show Results for Calculation
-ExportResults.showScoreTime(dir, fileName + "-TotalTime.png", store, score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation \n DB: ' + args.name + 'Feat: ' + args.feature, 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
-ExportResults.showScoreTime(dir, fileName + "-FeatExtTime.png", store, score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation \n DB: ' + args.name + 'Feat: ' + args.feature, 'Precision', 'Feature Extraction Time\n [s]')
-ExportResults.showScoreTime(dir, fileName + "-ClassTime.png", store, score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation \n DB: ' + args.name + 'Feat: ' + args.feature, 'Precision', 'Classification Time\n [s]')
+ExportResults.showScoreTime(dir, fileName + "-TotalTime", store, score, tot_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation - DB:' + args.name + ' Feat:' + args.feature, 'Precision', 'Total Time (Feature Extraction+Classification)\n [s]')
+ExportResults.showScoreTime(dir, fileName + "-FeatExtTime", store, score, feat_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation - DB:' + args.name + ' Feat:' + args.feature, 'Precision', 'Feature Extraction Time\n [s]')
+ExportResults.showScoreTime(dir, fileName + "-ClassTime", store, score, cl_time, rangeX, args.param, feat_desc, cl_desc, 'Results for Parameter Optimisation - DB:' + args.name + ' Feat:' + args.feature, 'Precision', 'Classification Time\n [s]')
logging.debug("### Done:\t Plot Result")
diff --git a/Code/Niko/ExportResults.py b/Code/Niko/ExportResults.py
index 1baa3edb..cdc3d90e 100644
--- a/Code/Niko/ExportResults.py
+++ b/Code/Niko/ExportResults.py
@@ -3,21 +3,22 @@
""" Library: Functions to export results to CSV or plots """
# Import built-in modules
-import os # for iteration throug directories
-import string # to generate a range of letters
+import os # for iteration throug directories
+import string # to generate a range of letters
# Import 3rd party modules
-import pandas as pd # for Series and DataFrames
-import numpy as np # for Numpy Arrays
-import matplotlib.pyplot as plt # for Plots
-from scipy.interpolate import interp1d # to Interpolate Data
-from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, HPacker # to generate the Annotations in plot
+import pandas as pd # for Series and DataFrames
+import numpy as np # for Numpy Arrays
+import matplotlib.pyplot as plt # for Plots
+from scipy.interpolate import interp1d # to Interpolate Data
+from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, HPacker # to generate the Annotations in plot
+from pylab import rcParams # to change size of plot
# Import own modules
# Author-Info
__author__ = "Nikolas Huelsmann"
-__status__ = "Prototype" # Production, Development, Prototype
+__status__ = "Prototype" # Production, Development, Prototype
__date__ = 2016-03-25
#### Export Features to CSV
@@ -57,9 +58,13 @@ def showScoreTime(dir, filename, store, resScore, resTime, rangeX, parameter, fe
f_score_interp = interp1d(rangeX, resScore, kind='quadratic')
f_time_interp = interp1d(rangeX, resTime, kind='quadratic')
+ # Change size of plot
+ rcParams['figure.figsize'] =20, 10
+
# Figure1 with subplot
fig, ax1 = plt.subplots()
+
#plt.plot(x, y, type of line)
# Generating X-Axis
xnew = np.linspace(0, max(rangeX), num=100, endpoint=True)
@@ -86,7 +91,7 @@ def showScoreTime(dir, filename, store, resScore, resTime, rangeX, parameter, fe
ax1.annotate(letter, xy=(act_x, act_score), xytext=(act_x, act_score))
ax2.annotate(letter, xy=(act_x, act_time), xytext=(act_x, act_time))
# Creates a legend with description of feature and classificator of each datapoint
- legend = legend + letter + ": " + act_feat_desc + " Classf: " + act_cl_desc + "\n"
+ legend = legend + letter + ") Feature: " + act_feat_desc + "; Classifier: " + act_cl_desc + "\n"
# Remove last \n
legend = legend[:-1]
@@ -99,9 +104,9 @@ def showScoreTime(dir, filename, store, resScore, resTime, rangeX, parameter, fe
anchored_box = AnchoredOffsetbox(loc=3,
child=box, pad=0.2,
frameon=True,
- bbox_to_anchor=(0., 1.02),
+ bbox_to_anchor=(0, 1.04), #to change the place of the legend (text above of figure)
bbox_transform=ax1.transAxes,
- borderpad=1.5,
+ borderpad=1.0,
)
ax1.add_artist(anchored_box)
fig.subplots_adjust(top=0.7)
diff --git a/Code/Niko/FeatExtraction.py b/Code/Niko/FeatExtraction.py
index ccc135b7..4f631fb0 100644
--- a/Code/Niko/FeatExtraction.py
+++ b/Code/Niko/FeatExtraction.py
@@ -193,9 +193,9 @@ def calcSURFSIFTDescriptors(dfImages, boolSIFT):
npImages = dfImages.values
if(boolSIFT==True):
- feat = "SIFT:\t "
+ feat = "### SIFT:\t "
else:
- feat = "SURF:\t "
+ feat = "### SURF:\t "
# List where all the descriptors are stored
des_list = []
@@ -316,9 +316,9 @@ def calcSURFSIFTHisto(nameDB, dfImages, cluster, boolNormMinMax, descriptors,des
param = "Cluster_" + str(cluster) + "-" + "Norm_" + norm
if(boolSIFT==True):
- feat="SIFT"
+ feat="### SIFT"
else:
- feat="SURF"
+ feat="### SURF"
description = nameDB + "-" + feat + "-" + param
#### Bag of Words Approach
diff --git a/Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB-LOG.log b/Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB-LOG.log
new file mode 100644
index 00000000..8ddbb1cb
--- /dev/null
+++ b/Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB-LOG.log
@@ -0,0 +1,16 @@
+2016-03-22 16:40:43,005 DEBUG:### Main Programm for Classification MonoView
+2016-03-22 16:40:43,005 DEBUG:### Info: Database:CTMini Feature:RGB train_size:0.8, GridSearch of Trees:50 100 150 200, CrossValidation k-folds:8, cores:1
+2016-03-22 16:40:43,006 DEBUG:### Start: Read CSV Files
+2016-03-22 16:40:43,016 DEBUG:### Info: Shape of Feature:(97L, 48L), Length of classLabels vector:(97L,)
+2016-03-22 16:40:43,016 DEBUG:### Done: Read CSV Files
+2016-03-22 16:40:43,016 DEBUG:### Start: Determine Train/Test split
+2016-03-22 16:40:43,016 DEBUG:### Info: Shape X_train:(77L, 48L), Length of y_train:77
+2016-03-22 16:40:43,017 DEBUG:### Info: Shape X_test:(20L, 48L), Length of y_test:20
+2016-03-22 16:40:43,017 DEBUG:### Done: Determine Train/Test split
+2016-03-22 16:40:43,017 DEBUG:### Start: Classification
+2016-03-22 16:40:45,684 DEBUG:### Info: Time for Classification: 2.67600011826[s]
+2016-03-22 16:40:45,684 DEBUG:### End: Classification
+2016-03-22 16:40:45,684 DEBUG:### Start: Exporting to CSV
+2016-03-22 16:40:45,690 DEBUG:### Done: Exporting to CSV
+2016-03-22 16:40:45,690 DEBUG:### Start: Plot Result
+2016-03-22 16:40:45,839 DEBUG:### Done: Plot Result
diff --git a/Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB.csv b/Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB.csv
new file mode 100644
index 00000000..2372e9cc
--- /dev/null
+++ b/Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB.csv
@@ -0,0 +1,12 @@
+;a_class_time;b_cl_desc;c_cl_res;d_cl_score
+0;2.67600011826;Classif_RF-CV_8-Trees_50;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";0.948051948052
diff --git a/Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB.png b/Code/Niko/Results-ClassMonoView/2016_03_22-CMV-CTMini-RGB.png
new file mode 100644
index 0000000000000000000000000000000000000000..7436b1b170954eb25349e7a9b6f10cded50857e8
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diff --git a/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-ClassLabels-Description.csv b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-ClassLabels-Description.csv
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+++ b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-ClassLabels-Description.csv
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new file mode 100644
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+++ b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-ClassLabels.csv
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diff --git a/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-HOG-CellDimension_5-nbOrientaions_8-nbClusters_12-Maxiter_100.csv b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-HOG-CellDimension_5-nbOrientaions_8-nbClusters_12-Maxiter_100.csv
new file mode 100644
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diff --git a/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-HSV-Bins_[16, 4, 4]-Norm_Distr.csv b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-HSV-Bins_[16, 4, 4]-Norm_Distr.csv
new file mode 100644
index 00000000..54aad8d1
--- /dev/null
+++ b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-HSV-Bins_[16, 4, 4]-Norm_Distr.csv
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new file mode 100644
index 00000000..6ddfcb80
--- /dev/null
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diff --git a/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-RGB_HSV_SIFT_SURF_HOG-LOG.log b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-RGB_HSV_SIFT_SURF_HOG-LOG.log
new file mode 100644
index 00000000..e8b44d98
--- /dev/null
+++ b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-RGB_HSV_SIFT_SURF_HOG-LOG.log
@@ -0,0 +1,41 @@
+2016-03-22 15:16:37,407 DEBUG:### Main Programm for Feature Extraction ###
+2016-03-22 15:16:37,408 DEBUG:Infos: NameDB=CTMini, Path=D:\CaltechMini, Features=RGB HSV SIFT SURF HOG
+2016-03-22 15:16:37,408 DEBUG:Start: Exportation of images from DB
+2016-03-22 15:16:37,667 DEBUG:Done: Exportation of images from DB in:0.259000062943[s]
+2016-03-22 15:16:37,668 DEBUG:Start: Features Extraction
+2016-03-22 15:16:37,668 DEBUG:RGB: Start
+2016-03-22 15:16:37,668 DEBUG:RGB: NumberOfBins=16, MaxColorIntensity=256, Norm=Distr
+2016-03-22 15:16:37,844 DEBUG:RGB: Done in: 0.175000190735[s]
+2016-03-22 15:16:37,844 DEBUG:HSV: Start
+2016-03-22 15:16:37,844 DEBUG:HSV: HSVBins=[16,4,4], Norm=Distr
+2016-03-22 15:16:38,188 DEBUG:HSV: Done in: 0.344999790192[s]
+2016-03-22 15:16:38,190 DEBUG:SIFT: Start
+2016-03-22 15:16:38,190 DEBUG:SIFT: Cluster=50, Norm=Distr
+2016-03-22 15:16:38,190 DEBUG:SIFT: Keypoints Calculation
+2016-03-22 15:16:40,349 DEBUG:SIFT: 25% of images processed (Keypoints)
+2016-03-22 15:16:42,005 DEBUG:SIFT: 50% of images processed (Keypoints)
+2016-03-22 15:16:43,447 DEBUG:SIFT: 75% of images processed (Keypoints)
+2016-03-22 15:16:44,878 DEBUG:SIFT: Calculation of Dictonary with 50 Clusters
+2016-03-22 15:16:48,861 DEBUG:SIFT: Assign words from Dictonary to each Image
+2016-03-22 15:16:48,951 DEBUG:SIFT: 25% of images processed (Assignments)
+2016-03-22 15:16:49,023 DEBUG:SIFT: 50% of images processed (Assignments)
+2016-03-22 15:16:49,079 DEBUG:SIFT: 75% of images processed (Assignments)
+2016-03-22 15:16:49,135 DEBUG:SIFT: Done in: 10.9459998608[s]
+2016-03-22 15:16:49,135 DEBUG:SURF: Start
+2016-03-22 15:16:49,137 DEBUG:SURF: Cluster=50, Norm=Distr
+2016-03-22 15:16:49,138 DEBUG:SURF: Keypoints Calculation
+2016-03-22 15:16:51,694 DEBUG:SURF: 25% of images processed (Keypoints)
+2016-03-22 15:16:53,861 DEBUG:SURF: 50% of images processed (Keypoints)
+2016-03-22 15:16:56,025 DEBUG:SURF: 75% of images processed (Keypoints)
+2016-03-22 15:16:58,243 DEBUG:SURF: Calculation of Dictonary with 50 Clusters
+2016-03-22 15:17:00,667 DEBUG:SURF: Assign words from Dictonary to each Image
+2016-03-22 15:17:00,757 DEBUG:SURF: 25% of images processed (Assignments)
+2016-03-22 15:17:00,829 DEBUG:SURF: 50% of images processed (Assignments)
+2016-03-22 15:17:00,897 DEBUG:SURF: 75% of images processed (Assignments)
+2016-03-22 15:17:00,986 DEBUG:SURF: Done in: 11.8489999771[s]
+2016-03-22 15:17:00,986 DEBUG:HOG: Start
+2016-03-22 15:17:00,986 DEBUG:HOG: CellDim=5, NbOrientations=8, Cluster=12, MaxIter=100, NbCores=1
+2016-03-22 15:19:21,826 DEBUG:HOG: Done in: 140.839999914[s]
+2016-03-22 15:19:21,828 DEBUG:Done: Features Extraction
+2016-03-22 15:19:21,828 DEBUG:Start: Save Features to CSV Databases
+2016-03-22 15:19:21,924 DEBUG:Done: Save Features to CSV Databases
diff --git a/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-SIFT-Cluster_50-Norm_Distr.csv b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-SIFT-Cluster_50-Norm_Distr.csv
new file mode 100644
index 00000000..f2fe4f60
--- /dev/null
+++ b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-SIFT-Cluster_50-Norm_Distr.csv
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diff --git a/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-SURF-Cluster_50-Norm_Distr.csv b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-SURF-Cluster_50-Norm_Distr.csv
new file mode 100644
index 00000000..aae07958
--- /dev/null
+++ b/Code/Niko/Results-FeatExtr/2016_03_22-FE-CTMini-SURF-Cluster_50-Norm_Distr.csv
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diff --git a/Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-LOG.log b/Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-LOG.log
new file mode 100644
index 00000000..1f46fb90
--- /dev/null
+++ b/Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-LOG.log
@@ -0,0 +1,50 @@
+2016-03-22 16:32:30,171 DEBUG:### Main Programm for Feature Parameter Optimisation
+2016-03-22 16:32:30,171 DEBUG:### Optimisation - Feature:RGB Parameter:RGB_Bins from:2 to:30 in #calc:10
+2016-03-22 16:32:30,173 DEBUG:### Start: Exportation of images from DB
+2016-03-22 16:32:30,434 DEBUG:### Done: Exportation of Images from DB
+2016-03-22 16:32:30,434 DEBUG:### Start: Feautre Optimisation
+2016-03-22 16:32:30,434 DEBUG:### Start: FeatureExtraction Nr:1 from:10 with RGB_Bins=2 ###
+2016-03-22 16:32:30,605 DEBUG:### Done: FeatureExtraction Nr:1 from:10 ###
+2016-03-22 16:32:30,607 DEBUG:### Start: Classification Nr:1 from:10
+2016-03-22 16:32:33,263 DEBUG:### Done: Classification Nr:1 from:10
+2016-03-22 16:32:33,269 DEBUG:### Start: FeatureExtraction Nr:2 from:10 with RGB_Bins=5 ###
+2016-03-22 16:32:33,440 DEBUG:### Done: FeatureExtraction Nr:2 from:10 ###
+2016-03-22 16:32:33,441 DEBUG:### Start: Classification Nr:2 from:10
+2016-03-22 16:32:36,063 DEBUG:### Done: Classification Nr:2 from:10
+2016-03-22 16:32:36,068 DEBUG:### Start: FeatureExtraction Nr:3 from:10 with RGB_Bins=8 ###
+2016-03-22 16:32:36,236 DEBUG:### Done: FeatureExtraction Nr:3 from:10 ###
+2016-03-22 16:32:36,237 DEBUG:### Start: Classification Nr:3 from:10
+2016-03-22 16:32:38,921 DEBUG:### Done: Classification Nr:3 from:10
+2016-03-22 16:32:38,926 DEBUG:### Start: FeatureExtraction Nr:4 from:10 with RGB_Bins=11 ###
+2016-03-22 16:32:39,098 DEBUG:### Done: FeatureExtraction Nr:4 from:10 ###
+2016-03-22 16:32:39,099 DEBUG:### Start: Classification Nr:4 from:10
+2016-03-22 16:32:41,841 DEBUG:### Done: Classification Nr:4 from:10
+2016-03-22 16:32:41,845 DEBUG:### Start: FeatureExtraction Nr:5 from:10 with RGB_Bins=14 ###
+2016-03-22 16:32:42,023 DEBUG:### Done: FeatureExtraction Nr:5 from:10 ###
+2016-03-22 16:32:42,023 DEBUG:### Start: Classification Nr:5 from:10
+2016-03-22 16:32:44,762 DEBUG:### Done: Classification Nr:5 from:10
+2016-03-22 16:32:44,769 DEBUG:### Start: FeatureExtraction Nr:6 from:10 with RGB_Bins=18 ###
+2016-03-22 16:32:44,950 DEBUG:### Done: FeatureExtraction Nr:6 from:10 ###
+2016-03-22 16:32:44,950 DEBUG:### Start: Classification Nr:6 from:10
+2016-03-22 16:32:47,818 DEBUG:### Done: Classification Nr:6 from:10
+2016-03-22 16:32:47,822 DEBUG:### Start: FeatureExtraction Nr:7 from:10 with RGB_Bins=21 ###
+2016-03-22 16:32:48,043 DEBUG:### Done: FeatureExtraction Nr:7 from:10 ###
+2016-03-22 16:32:48,045 DEBUG:### Start: Classification Nr:7 from:10
+2016-03-22 16:32:51,453 DEBUG:### Done: Classification Nr:7 from:10
+2016-03-22 16:32:51,459 DEBUG:### Start: FeatureExtraction Nr:8 from:10 with RGB_Bins=24 ###
+2016-03-22 16:32:51,677 DEBUG:### Done: FeatureExtraction Nr:8 from:10 ###
+2016-03-22 16:32:51,677 DEBUG:### Start: Classification Nr:8 from:10
+2016-03-22 16:32:54,657 DEBUG:### Done: Classification Nr:8 from:10
+2016-03-22 16:32:54,661 DEBUG:### Start: FeatureExtraction Nr:9 from:10 with RGB_Bins=27 ###
+2016-03-22 16:32:54,859 DEBUG:### Done: FeatureExtraction Nr:9 from:10 ###
+2016-03-22 16:32:54,861 DEBUG:### Start: Classification Nr:9 from:10
+2016-03-22 16:32:57,914 DEBUG:### Done: Classification Nr:9 from:10
+2016-03-22 16:32:57,920 DEBUG:### Start: FeatureExtraction Nr:10 from:10 with RGB_Bins=30 ###
+2016-03-22 16:32:58,135 DEBUG:### Done: FeatureExtraction Nr:10 from:10 ###
+2016-03-22 16:32:58,138 DEBUG:### Start: Classification Nr:10 from:10
+2016-03-22 16:33:01,158 DEBUG:### Done: Classification Nr:10 from:10
+2016-03-22 16:33:01,164 DEBUG:### Done: Feautre Optimisation
+2016-03-22 16:33:01,164 DEBUG:### Start: Exporting to CSV
+2016-03-22 16:33:01,173 DEBUG:### Done: Exporting to CSV
+2016-03-22 16:33:01,173 DEBUG:### Start: Plot Result
+2016-03-22 16:33:03,180 DEBUG:### Done: Plot Result
diff --git a/Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-TotalTime.png b/Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins-TotalTime.png
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diff --git a/Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins.csv b/Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins.csv
new file mode 100644
index 00000000..c0b29f78
--- /dev/null
+++ b/Code/Niko/Results-FeatParaOpt/2016_03_22-FPO-CTMini-RGB-RGB_Bins.csv
@@ -0,0 +1,111 @@
+;a_feat_desc;b_feat_extr_time;c_cl_desc;d_cl_res;e_cl_time;f_cl_score
+0;CTMini-RGB-Bins_2-MaxCI_256-Norm_Distr;0.168999910355;Classif_RF-CV_8-Trees_100;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";2.65900015831;0.805194805195
+1;CTMini-RGB-Bins_5-MaxCI_256-Norm_Distr;0.171999931335;Classif_RF-CV_8-Trees_50;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";2.62199997902;0.883116883117
+2;CTMini-RGB-Bins_8-MaxCI_256-Norm_Distr;0.167999982834;Classif_RF-CV_8-Trees_100;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";2.68400001526;0.922077922078
+3;CTMini-RGB-Bins_11-MaxCI_256-Norm_Distr;0.171999931335;Classif_RF-CV_8-Trees_50;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";2.74300003052;0.948051948052
+4;CTMini-RGB-Bins_14-MaxCI_256-Norm_Distr;0.177000045776;Classif_RF-CV_8-Trees_50;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";2.7380001545;0.961038961039
+5;CTMini-RGB-Bins_18-MaxCI_256-Norm_Distr;0.180999994278;Classif_RF-CV_8-Trees_100;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";2.86800003052;0.948051948052
+6;CTMini-RGB-Bins_21-MaxCI_256-Norm_Distr;0.22000002861;Classif_RF-CV_8-Trees_100;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";3.40899991989;0.935064935065
+7;CTMini-RGB-Bins_24-MaxCI_256-Norm_Distr;0.21799993515;Classif_RF-CV_8-Trees_50;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";2.98099994659;0.935064935065
+8;CTMini-RGB-Bins_27-MaxCI_256-Norm_Distr;0.196999788284;Classif_RF-CV_8-Trees_200;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";3.0529999733;0.974025974026
+9;CTMini-RGB-Bins_30-MaxCI_256-Norm_Distr;0.214999914169;Classif_RF-CV_8-Trees_100;"GridSearchCV(cv=8, error_score='raise',
+ estimator=Pipeline(steps=[('classifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
+ max_depth=None, max_features='auto', max_leaf_nodes=None,
+ min_samples_leaf=1, min_samples_split=2,
+ min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
+ oob_score=False, random_state=None, verbose=0,
+ warm_start=False))]),
+ fit_params={}, iid=True, loss_func=None, n_jobs=1,
+ param_grid={'classifier__n_estimators': [50, 100, 150, 200]},
+ pre_dispatch='2*n_jobs', refit=True, score_func=None,
+ scoring='accuracy', verbose=0)";3.02300000191;0.961038961039
--
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