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hy.kim
1 year ago
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,148 @@
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1. Title: Mushroom Database
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2. Sources:
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(a) Mushroom records drawn from The Audubon Society Field Guide to North
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American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred
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A. Knopf
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(b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
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(c) Date: 27 April 1987
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3. Past Usage:
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1. Schlimmer,J.S. (1987). Concept Acquisition Through Representational
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Adjustment (Technical Report 87-19). Doctoral disseration, Department
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of Information and Computer Science, University of California, Irvine.
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--- STAGGER: asymptoted to 95% classification accuracy after reviewing
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1000 instances.
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2. Iba,W., Wogulis,J., & Langley,P. (1988). Trading off Simplicity
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and Coverage in Incremental Concept Learning. In Proceedings of
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the 5th International Conference on Machine Learning, 73-79.
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Ann Arbor, Michigan: Morgan Kaufmann.
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-- approximately the same results with their HILLARY algorithm
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3. In the following references a set of rules (given below) were
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learned for this data set which may serve as a point of
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comparison for other researchers.
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Duch W, Adamczak R, Grabczewski K (1996) Extraction of logical rules
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from training data using backpropagation networks, in: Proc. of the
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The 1st Online Workshop on Soft Computing, 19-30.Aug.1996, pp. 25-30,
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available on-line at: http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/
|
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|
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Duch W, Adamczak R, Grabczewski K, Ishikawa M, Ueda H, Extraction of
|
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crisp logical rules using constrained backpropagation networks -
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comparison of two new approaches, in: Proc. of the European Symposium
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on Artificial Neural Networks (ESANN'97), Bruge, Belgium 16-18.4.1997,
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pp. xx-xx
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Wlodzislaw Duch, Department of Computer Methods, Nicholas Copernicus
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University, 87-100 Torun, Grudziadzka 5, Poland
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e-mail: duch@phys.uni.torun.pl
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WWW http://www.phys.uni.torun.pl/kmk/
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Date: Mon, 17 Feb 1997 13:47:40 +0100
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From: Wlodzislaw Duch <duch@phys.uni.torun.pl>
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Organization: Dept. of Computer Methods, UMK
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I have attached a file containing logical rules for mushrooms.
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It should be helpful for other people since only in the last year I
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have seen about 10 papers analyzing this dataset and obtaining quite
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complex rules. We will try to contribute other results later.
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With best regards, Wlodek Duch
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________________________________________________________________
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Logical rules for the mushroom data sets.
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Logical rules given below seem to be the simplest possible for the
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mushroom dataset and therefore should be treated as benchmark results.
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Disjunctive rules for poisonous mushrooms, from most general
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to most specific:
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P_1) odor=NOT(almond.OR.anise.OR.none)
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120 poisonous cases missed, 98.52% accuracy
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P_2) spore-print-color=green
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48 cases missed, 99.41% accuracy
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P_3) odor=none.AND.stalk-surface-below-ring=scaly.AND.
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(stalk-color-above-ring=NOT.brown)
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8 cases missed, 99.90% accuracy
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P_4) habitat=leaves.AND.cap-color=white
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100% accuracy
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Rule P_4) may also be
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P_4') population=clustered.AND.cap_color=white
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These rule involve 6 attributes (out of 22). Rules for edible
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mushrooms are obtained as negation of the rules given above, for
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example the rule:
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odor=(almond.OR.anise.OR.none).AND.spore-print-color=NOT.green
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gives 48 errors, or 99.41% accuracy on the whole dataset.
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Several slightly more complex variations on these rules exist,
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involving other attributes, such as gill_size, gill_spacing,
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stalk_surface_above_ring, but the rules given above are the simplest
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we have found.
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4. Relevant Information:
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This data set includes descriptions of hypothetical samples
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corresponding to 23 species of gilled mushrooms in the Agaricus and
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Lepiota Family (pp. 500-525). Each species is identified as
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definitely edible, definitely poisonous, or of unknown edibility and
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not recommended. This latter class was combined with the poisonous
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one. The Guide clearly states that there is no simple rule for
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determining the edibility of a mushroom; no rule like ``leaflets
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three, let it be'' for Poisonous Oak and Ivy.
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5. Number of Instances: 8124
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6. Number of Attributes: 22 (all nominally valued)
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7. Attribute Information: (classes: edible=e, poisonous=p)
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1. cap-shape: bell=b,conical=c,convex=x,flat=f,
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knobbed=k,sunken=s
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2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s
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3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r,
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pink=p,purple=u,red=e,white=w,yellow=y
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4. bruises?: bruises=t,no=f
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5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f,
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musty=m,none=n,pungent=p,spicy=s
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6. gill-attachment: attached=a,descending=d,free=f,notched=n
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7. gill-spacing: close=c,crowded=w,distant=d
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8. gill-size: broad=b,narrow=n
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9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g,
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green=r,orange=o,pink=p,purple=u,red=e,
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white=w,yellow=y
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10. stalk-shape: enlarging=e,tapering=t
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11. stalk-root: bulbous=b,club=c,cup=u,equal=e,
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rhizomorphs=z,rooted=r,missing=?
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12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
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13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
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14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,
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pink=p,red=e,white=w,yellow=y
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15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o,
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pink=p,red=e,white=w,yellow=y
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16. veil-type: partial=p,universal=u
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17. veil-color: brown=n,orange=o,white=w,yellow=y
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18. ring-number: none=n,one=o,two=t
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19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l,
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none=n,pendant=p,sheathing=s,zone=z
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20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r,
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orange=o,purple=u,white=w,yellow=y
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21. population: abundant=a,clustered=c,numerous=n,
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scattered=s,several=v,solitary=y
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22. habitat: grasses=g,leaves=l,meadows=m,paths=p,
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urban=u,waste=w,woods=d
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8. Missing Attribute Values: 2480 of them (denoted by "?"), all for
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attribute #11.
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9. Class Distribution:
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-- edible: 4208 (51.8%)
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-- poisonous: 3916 (48.2%)
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-- total: 8124 instances
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@ -0,0 +1,125 @@
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//Example 21-1. Creating and training a decision tree
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#include <opencv2/opencv.hpp>
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#include <stdio.h>
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#include <iostream>
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using namespace std;
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using namespace cv;
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void help(char **argv) {
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cout << "\n\n"
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<< "Using binary decision trees to learn to recognize poisonous\n"
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<< " from edible mushrooms based on visible attributes.\n"
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<< " This program demonstrates how to create and a train a \n"
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<< " decision tree using ml library in OpenCV.\n"
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<< "Call:\n" << argv[0] << " <csv-file-path>\n\n"
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<< "\nIf you don't enter a file, it defaults to agaricus-lepiota.data\n"
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<< endl;
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}
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int main(int argc, char *argv[]) {
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// If the caller gave a filename, great. Otherwise, use a default.
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//
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//const char *csv_file_name = argc >= 2 ? argv[1] : "../mushroom/agaricus-lepiota.data";
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const char *csv_file_name = "agaricus-lepiota.data";
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cout << "OpenCV Version: " << CV_VERSION << endl;
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help(argv);
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// Read in the CSV file that we were given.
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//
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cv::Ptr<cv::ml::TrainData> data_set =
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cv::ml::TrainData::loadFromCSV(csv_file_name, // Input file name
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0, // Header lines (ignore this many)
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0, // Responses are (start) at thie column
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1, // Inputs start at this column
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"cat[0-22]" // All 23 columns are categorical
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);
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// Use defaults for delimeter (',') and missch ('?')
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// Verify that we read in what we think.
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//
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int n_samples = data_set->getNSamples();
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if (n_samples == 0) {
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cerr << "Could not read file: " << csv_file_name << endl;
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exit(-1);
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}
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else {
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cout << "Read " << n_samples << " samples from " << csv_file_name << endl;
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}
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// Split the data, so that 90% is train data
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//
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data_set->setTrainTestSplitRatio(0.90, false);
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int n_train_samples = data_set->getNTrainSamples();
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||||
int n_test_samples = data_set->getNTestSamples();
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cout << "Found " << n_train_samples << " Train Samples, and "
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<< n_test_samples << " Test Samples" << endl;
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|
||||
// Create a DTrees classifier.
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//
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cv::Ptr<cv::ml::RTrees> dtree = cv::ml::RTrees::create();
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||||
// set parameters
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||||
//
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||||
// These are the parameters from the old mushrooms.cpp code
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||||
// Set up priors to penalize "poisonous" 10x as much as "edible"
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||||
//
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float _priors[] = { 1.0, 10.0 };
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cv::Mat priors(1, 2, CV_32F, _priors);
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dtree->setMaxDepth(8);
|
||||
dtree->setMinSampleCount(10);
|
||||
dtree->setRegressionAccuracy(0.01f);
|
||||
dtree->setUseSurrogates(false /* true */);
|
||||
dtree->setMaxCategories(15);
|
||||
dtree->setCVFolds(0 /*10*/); // nonzero causes core dump
|
||||
dtree->setUse1SERule(true);
|
||||
dtree->setTruncatePrunedTree(true);
|
||||
dtree->setPriors( priors );
|
||||
//dtree->setPriors(cv::Mat()); // ignore priors for now...
|
||||
// Now train the model
|
||||
// NB: we are only using the "train" part of the data set
|
||||
//
|
||||
dtree->train(data_set);
|
||||
|
||||
// Having successfully trained the data, we should be able
|
||||
// to calculate the error on both the training data, as well
|
||||
// as the test data that we held out.
|
||||
//
|
||||
cv::Mat results;
|
||||
float train_performance = dtree->calcError(data_set,
|
||||
false, // use train data
|
||||
results // cv::noArray()
|
||||
);
|
||||
std::vector<cv::String> names;
|
||||
data_set->getNames(names);
|
||||
Mat flags = data_set->getVarSymbolFlags();
|
||||
|
||||
// Compute some statistics on our own:
|
||||
//
|
||||
{
|
||||
cv::Mat expected_responses = data_set->getResponses();
|
||||
int good = 0, bad = 0, total = 0;
|
||||
for (int i = 0; i < data_set->getNTrainSamples(); ++i) {
|
||||
float received = results.at<float>(i, 0);
|
||||
float expected = expected_responses.at<float>(i, 0);
|
||||
cv::String r_str = names[(int)received];
|
||||
cv::String e_str = names[(int)expected];
|
||||
cout << "Expected: " << e_str << ", got: " << r_str << endl;
|
||||
if (received == expected)
|
||||
good++;
|
||||
else
|
||||
bad++;
|
||||
total++;
|
||||
}
|
||||
cout << "Correct answers: " << (float(good) / total) << " % " << endl;
|
||||
cout << "Incorrect answers: " << (float(bad) / total) << "%"
|
||||
<< endl;
|
||||
}
|
||||
float test_performance = dtree->calcError(data_set,
|
||||
true, // use test data
|
||||
results // cv::noArray()
|
||||
);
|
||||
cout << "Performance on training data: " << train_performance << "%" << endl;
|
||||
cout << "Performance on test data: " << test_performance << " % " << endl;
|
||||
return 0;
|
||||
}
|
||||
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Reference in New Issue