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https://github.com/Doctorado-ML/FImdlp.git
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56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
import time
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import argparse
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import os
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from scipy.io import arff
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import pandas as pd
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from sklearn.ensemble import RandomForestClassifier
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from fimdlp.mdlp import FImdlp
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datasets = {
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"mfeat-factors": True,
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"iris": True,
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"glass": True,
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"liver-disorders": True,
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"letter": True,
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"kdd_JapaneseVowels": False,
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}
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ap = argparse.ArgumentParser()
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ap.add_argument(
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"--min_length", type=int, default=3, help="Minimum length of interval"
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)
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ap.add_argument("--max_depth", type=int, default=9999, help="Maximum depth")
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ap.add_argument(
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"--max_cuts", type=float, default=0, help="Maximum number of cut points"
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)
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ap.add_argument("dataset", type=str, choices=datasets.keys())
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args = ap.parse_args()
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relative = "" if os.path.isdir("src") else ".."
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file_name = os.path.join(
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relative, "src", "cppmdlp", "tests", "datasets", args.dataset
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)
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data = arff.loadarff(file_name + ".arff")
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df = pd.DataFrame(data[0])
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class_column = -1 if datasets[args.dataset] else 0
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class_name = df.columns.to_list()[class_column]
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X = df.drop(class_name, axis=1)
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y, _ = pd.factorize(df[class_name])
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X = X.to_numpy()
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test = FImdlp(
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min_length=args.min_length,
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max_depth=args.max_depth,
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max_cuts=args.max_cuts,
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)
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now = time.time()
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test.fit(X, y)
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fit_time = time.time()
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print(f"Fitting ....: {fit_time - now:7.5f} seconds")
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now = time.time()
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Xt = test.transform(X)
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print(f"Transforming: {time.time() - now:7.5f} seconds")
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print(test.get_cut_points())
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clf = RandomForestClassifier(random_state=0)
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print(
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"Random Forest score with discretized data: ", clf.fit(Xt, y).score(Xt, y)
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)
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