mirror of
https://github.com/Doctorado-ML/STree.git
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Ricardo Montañana
f360a2640c
Removed (by now) predict_proba. Created a notebook in jupyter Added split_criteria parameter with min_distance and max_samples values Refactor _distances Refactor _split_criteria Refactor _reorder_results
78 lines
2.2 KiB
Python
78 lines
2.2 KiB
Python
import time
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from sklearn.model_selection import train_test_split
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from stree import Stree
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random_state = 1
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def load_creditcard(n_examples=0):
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import pandas as pd
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import numpy as np
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import random
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df = pd.read_csv("data/creditcard.csv")
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print(
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"Fraud: {0:.3f}% {1}".format(
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df.Class[df.Class == 1].count() * 100 / df.shape[0],
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df.Class[df.Class == 1].count(),
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)
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)
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print(
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"Valid: {0:.3f}% {1}".format(
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df.Class[df.Class == 0].count() * 100 / df.shape[0],
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df.Class[df.Class == 0].count(),
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)
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)
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y = np.expand_dims(df.Class.values, axis=1)
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X = df.drop(["Class", "Time", "Amount"], axis=1).values
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if n_examples > 0:
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# Take first n_examples samples
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X = X[:n_examples, :]
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y = y[:n_examples, :]
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else:
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# Take all the positive samples with a number of random negatives
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if n_examples < 0:
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Xt = X[(y == 1).ravel()]
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yt = y[(y == 1).ravel()]
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indices = random.sample(range(X.shape[0]), -1 * n_examples)
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X = np.append(Xt, X[indices], axis=0)
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y = np.append(yt, y[indices], axis=0)
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print("X.shape", X.shape, " y.shape", y.shape)
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print(
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"Fraud: {0:.3f}% {1}".format(
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len(y[y == 1]) * 100 / X.shape[0], len(y[y == 1])
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)
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)
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print(
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"Valid: {0:.3f}% {1}".format(
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len(y[y == 0]) * 100 / X.shape[0], len(y[y == 0])
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)
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)
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Xtrain, Xtest, ytrain, ytest = train_test_split(
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X,
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y,
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train_size=0.7,
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shuffle=True,
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random_state=random_state,
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stratify=y,
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)
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return Xtrain, Xtest, ytrain, ytest
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# data = load_creditcard(-5000) # Take all true samples + 5000 of the others
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# data = load_creditcard(5000) # Take the first 5000 samples
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data = load_creditcard() # Take all the samples
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Xtrain = data[0]
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Xtest = data[1]
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ytrain = data[2]
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ytest = data[3]
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now = time.time()
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clf = Stree(C=0.01, random_state=random_state)
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clf.fit(Xtrain, ytrain)
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print(f"Took {time.time() - now:.2f} seconds to train")
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print(clf)
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print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}")
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print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}")
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