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https://github.com/Doctorado-ML/FImdlp.git
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46 lines
1.1 KiB
Python
46 lines
1.1 KiB
Python
from sklearn.datasets import load_iris
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from fimdlp.mdlp import FImdlp
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from fimdlp.cppfimdlp import CFImdlp
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from sklearn.ensemble import RandomForestClassifier
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import numpy as np
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import time
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from scipy.io import arff
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import pandas as pd
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# class_name = "speaker"
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# file_name = "kdd_JapaneseVowels.arff"
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class_name = "class"
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# file_name = "mfeat-factors.arff"
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file_name = "letter.arff"
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data = arff.loadarff(file_name)
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df = pd.DataFrame(data[0])
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df.dropna(axis=0, how="any", inplace=True)
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dataset = df
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X = df.drop(class_name, axis=1)
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features = X.columns
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class_name = class_name
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y, _ = pd.factorize(df[class_name])
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X = X.to_numpy()
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# data = load_iris()
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# X = data.data
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# y = data.target
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# features = data.feature_names
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test = FImdlp()
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now = time.time()
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# test.fit(X, y, features=[i for i in (range(3, 14))])
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test.fit(X, y)
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fit_time = time.time()
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print("Fitting: ", fit_time - now)
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now = time.time()
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Xt = test.transform(X)
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print("Transforming: ", time.time() - now)
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print(test.get_cut_points())
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clf = RandomForestClassifier(random_state=0)
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print(clf.fit(Xt, y).score(Xt, y))
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print(Xt)
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