94 lines
3.8 KiB
Python
94 lines
3.8 KiB
Python
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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import quapy as qp
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from quapy.method.aggregative import PACC
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from quapy.data import LabelledCollection
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from quapy.protocol import AbstractStochasticSeededProtocol
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import quapy.functional as F
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"""
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In this example, we create a custom protocol.
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The protocol generates samples of a Gaussian mixture model with random mixture parameter (the sample prevalence).
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Datapoints are univariate and we consider 2 classes only.
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"""
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class GaussianMixProtocol(AbstractStochasticSeededProtocol):
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# We need to extend AbstractStochasticSeededProtocol if we want the samples to be replicable
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def __init__(self, mu_1:float, std_1:float, mu_2:float, std_2:float, num_samples, sample_size, random_state=0):
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super(GaussianMixProtocol, self).__init__(random_state) # this sets the random state
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self.mu_1 = mu_1
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self.std_1 = std_1
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self.mu_2 = mu_2
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self.std_2 = std_2
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self.num_samples = num_samples
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self.sample_size = sample_size
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def samples_parameters(self):
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# This function is inherited and has to be overriden.
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# This function should return all the necessary parameters for producing the samples.
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# In this case, we consider returning a vector of seeds (one for each sample) and a vector of
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# randomly sampled prevalence values.
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# This function will be invoked within a context that sets the seed, so it will always return the
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# same parameters. In case you want different outcomes, then simply set random_state=None.
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rand_offset = np.random.randint(1000)
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sample_seeds = np.random.permutation(self.num_samples*2) + rand_offset
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random_prevs = np.random.rand(self.num_samples)
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params = np.hstack([sample_seeds.reshape(-1,2), random_prevs.reshape(-1,1)])
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# each row in params contains two seeds (for generating the negatives and the positives, respectively) and
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# the prevalence vector
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return params
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def sample(self, params):
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# the params are two seeds and the positive prevalence of the sample
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seed0, seed1, pos_prev = params
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num_positives = int(pos_prev * self.sample_size)
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num_negatives = self.sample_size - num_positives
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with qp.util.temp_seed(int(seed0)):
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Xneg = np.random.normal(loc=self.mu_1, scale=self.std_1, size=num_negatives)
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with qp.util.temp_seed(int(seed1)):
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Xpos = np.random.normal(loc=self.mu_2, scale=self.std_2, size=num_positives)
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X = np.concatenate((Xneg,Xpos))
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np.random.shuffle(X)
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X = X.reshape(-1,1)
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prev = F.as_binary_prevalence(pos_prev)
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return X, prev
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def total(self):
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# overriding this function will allow some methods display a meaningful progress bar
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return self.num_samples
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mu_1, std_1 = 0, 1
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mu_2, std_2 = 1, 1
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gm = GaussianMixProtocol(mu_1=mu_1, std_1=std_1, mu_2=mu_2, std_2=std_2, num_samples=10, sample_size=50)
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# let's see if the samples are replicated
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for i, (X, prev) in enumerate(gm()):
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if i>4: break
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print(f'sample-{i}: {F.strprev(prev)}, some covariates={X[:5].flatten()}...')
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print()
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for i, (X, prev) in enumerate(gm()):
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if i > 4: break
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print(f'sample-{i}: {F.strprev(prev)}, some covariates={X[:5].flatten()}...')
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# let's generate some training data
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# The samples are replicable, but by setting a temp seed we achieve repicable training as well
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with qp.util.temp_seed(0):
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Xneg = np.random.normal(loc=mu_1, scale=std_1, size=100)
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Xpos = np.random.normal(loc=mu_2, scale=std_2, size=100)
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X = np.concatenate([Xneg, Xpos]).reshape(-1,1)
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y = [0]*100 + [1]*100
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training = LabelledCollection(X, y)
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pacc = PACC(LogisticRegression())
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pacc.fit(training)
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mae = qp.evaluation.evaluate(pacc, protocol=gm, error_metric='mae', verbose=True)
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print(f'PACC MAE={mae:.5f}')
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