46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
from sklearn import tree
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from sklearn import ensemble
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import numpy as np
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def gmean(a, axis=0, dtype=None):
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if not isinstance(a, np.ndarray): # if not an ndarray object attempt to convert it
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log_a = np.log(np.array(a, dtype=dtype))
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elif dtype: # Must change the default dtype allowing array type
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if isinstance(a,np.ma.MaskedArray):
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log_a = np.log(np.ma.asarray(a, dtype=dtype))
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else:
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log_a = np.log(np.asarray(a, dtype=dtype))
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else:
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log_a = np.log(a)
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return np.exp(log_a.mean(axis=axis))
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def train_model(X, Y, profiles, metric):
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#Shuffle
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p = np.random.permutation(X.shape[0])
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X = X[p,:]
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Y = Y[p,:]
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#Normalize
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Ymax = np.max(Y)
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Y = Y/Ymax
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#Train the model
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cut = int(0.75*X.shape[0])
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clf = ensemble.RandomForestRegressor(10, max_depth=3).fit(X[:cut,:], Y[:cut,:])
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t = np.argmin(clf.predict(X[cut:,:]), axis = 1)
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s = np.array([y[0]/y[k] for y,k in zip(Y[cut:,:], t)])
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tt = np.argmin(Y[cut:,:], axis = 1)
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ss = np.array([y[0]/y[k] for y,k in zip(Y[cut:,:], tt)])
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p5 = lambda a: np.percentile(a, 5)
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p25 = lambda a: np.percentile(a, 25)
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p50 = lambda a: np.percentile(a, 50)
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p75 = lambda a: np.percentile(a, 75)
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p95 = lambda a: np.percentile(a, 95)
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print("Percentile :\t 5 \t 25 \t 50 \t 75 \t 95")
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print("Testing speedup:\t %.2f\t %.2f\t %.2f\t %.2f\t %.3f"%(p5(s), p25(s), p50(s), p75(s), p95(s)))
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print("Optimal speedup:\t %.2f\t %.2f\t %.2f\t %.2f\t %.3f"%(p5(ss), p25(ss), p50(ss), p75(ss), p95(ss)))
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return clf
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