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arf and csv test

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This commit is contained in:
Ricardo Montañana Gómez
2021-06-03 23:33:54 +02:00
parent 7404566e7a
commit ff80fe6172
4 changed files with 1296 additions and 2 deletions
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632
balance-scale.arff Executable file
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@@ -0,0 +1,632 @@
@relation balance-scale
@attribute 'f1' real
@attribute 'f2' real
@attribute 'f3' real
@attribute 'f4' real
@attribute 'clase' {0,1,2}
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38
k.py
View File

@@ -3,10 +3,35 @@ from sklearn.datasets import load_wine
from mfs import MFS from mfs import MFS
from mfs.Metrics import Metrics from mfs.Metrics import Metrics
from stree import Stree from stree import Stree
import numpy as np
from scipy.io import arff
from sklearn.preprocessing import StandardScaler
mfsc = MFS(discrete=False) mfsc = MFS(discrete=False)
mfsd = MFS(discrete=True) mfsd = MFS(discrete=True)
X, y = load_wine(return_X_y=True)
# Xo, y = load_wine(return_X_y=True)
# X = Xo.copy()
# scaler = StandardScaler()
# for c in range(X.shape[1]):
# X[:, c] = scaler.fit_transform(X[:, c].reshape(-1, 1)).reshape(-1)
# data = np.genfromtxt("balance-scale.csv")
# X = data[:, -1:]
# y = data[:, -1]
data, meta = arff.loadarff(
"/Users/rmontanana/Code/stree_datasets/data/tanveer/balance-scale/balance-scale.arff"
)
train = np.array([data["f1"], data["f2"], data["f3"], data["f4"]])
y = data["clase"]
X = train.T
for c in range(X.shape[1]):
print(f"Mean: {np.mean(X[:,c])} Std: {np.std(X[:,c])}")
m, n = X.shape m, n = X.shape
print("* Differential entropy in X") print("* Differential entropy in X")
for i in range(n): for i in range(n):
@@ -39,5 +64,14 @@ clf = Stree(random_state=0)
print("cfs discreto", clf.fit(X[:, cfs_d], y).score(X[:, cfs_d], y)) print("cfs discreto", clf.fit(X[:, cfs_d], y).score(X[:, cfs_d], y))
print("cfs continuo", clf.fit(X[:, cfs_f], y).score(X[:, cfs_f], y)) print("cfs continuo", clf.fit(X[:, cfs_f], y).score(X[:, cfs_f], y))
clf = Stree(random_state=0) clf = Stree(random_state=0)
subf = fcfb_f[:6] # subf = fcfb_f[:6]
subf = fcfb_f
print("fcfb", clf.fit(X[:, subf], y).score(X[:, subf], y)) print("fcfb", clf.fit(X[:, subf], y).score(X[:, subf], y))
for c in range(X.shape[1]):
for k in range(X.shape[1]):
ac = 0
for v in range(X[:, c].shape[0]):
if X[v, c] == X[v, k]:
ac += 1
print(f"{c} {k} {ac}")

3
mfs/Selection.py
View File

@@ -157,6 +157,9 @@ class MFS:
id_selected = idx id_selected = idx
merit = merit_new merit = merit_new
candidates.pop() candidates.pop()
if id_selected is None:
# Every merit computed is 0
break
candidates.append(feature_order[id_selected]) candidates.append(feature_order[id_selected])
self._scores.append(merit) self._scores.append(merit)
del feature_order[id_selected] del feature_order[id_selected]