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| Original file line number | Diff line number | Diff line change |
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| from sklearn.base import BaseEstimator, ClassifierMixin | ||
| from sklearn.utils import check_X_y, check_array | ||
| from timbl import TimblClassifier | ||
| import scipy as sp | ||
| import numpy as np | ||
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| class skTiMBL(BaseEstimator, ClassifierMixin): | ||
| def __init__(self, prefix='timbl', algorithm=4, dist_metric=None, | ||
| k=1, normalize=False, debug=0, flushdir=None): | ||
| self.prefix = prefix | ||
| self.algorithm = algorithm | ||
| self.dist_metric = dist_metric | ||
| self.k = k | ||
| self.normalize = normalize | ||
| self.debug = debug | ||
| self.flushdir = flushdir | ||
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| def _make_timbl_options(self, *options): | ||
| """ | ||
| -a algorithm | ||
| -m metric | ||
| -w weighting | ||
| -k amount of neighbours | ||
| -d class voting weights | ||
| -L frequency threshold | ||
| -T which feature index is label | ||
| -N max number of features | ||
| -H turn hashing on/off | ||
| This function still has to be made, for now the appropriate arguments | ||
| can be passed in fit() | ||
| """ | ||
| pass | ||
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| def fit(self, X, y): | ||
| X, y = check_X_y(X, y, dtype=np.int64, accept_sparse='csr') | ||
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| n_rows = X.shape[0] | ||
| self.classes_ = np.unique(y) | ||
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| if sp.sparse.issparse(X): | ||
| if self.debug: print('Features are sparse, choosing faster learning') | ||
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| self.classifier = TimblClassifier(self.prefix, "-a{} -k{} -N{} -vf".format(self.algorithm,self.k, X.shape[1]), | ||
| format='Sparse', debug=True, sklearn=True, flushdir=self.flushdir, | ||
| flushthreshold=20000, normalize=self.normalize) | ||
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| for i in range(n_rows): | ||
| sparse = ['({},{})'.format(i+1, c) for i,c in zip(X[i].indices, X[i].data)] | ||
| self.classifier.append(sparse,str(y[i])) | ||
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| else: | ||
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| self.classifier = TimblClassifier(self.prefix, "-a{} -k{} -N{} -vf".format(self.algorithm, self.k, X.shape[1]), | ||
| debug=True, sklearn=True, flushdir=self.flushdir, flushthreshold=20000, | ||
| normalize=self.normalize) | ||
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| if y.dtype != 'O': | ||
| y = y.astype(str) | ||
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| for i in range(n_rows): | ||
| self.classifier.append(list(X[i].toarray()[0]), y[i]) | ||
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| self.classifier.train() | ||
| return self | ||
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| def _timbl_predictions(self, X, part_index, y=None): | ||
| choices = {0 : lambda x : x.append(np.int64(label)), | ||
| 1 : lambda x : x.append([np.float(distance)]), | ||
| } | ||
| X = check_array(X, dtype=np.float64, accept_sparse='csr') | ||
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| n_samples = X.shape[0] | ||
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| pred = [] | ||
| func = choices[part_index] | ||
| if sp.sparse.issparse(X): | ||
| if self.debug: print('Features are sparse, choosing faster predictions') | ||
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| for i in range(n_samples): | ||
| sparse = ['({},{})'.format(i+1, c) for i,c in zip(X[i].indices, X[i].data)] | ||
| label,proba, distance = self.classifier.classify(sparse) | ||
| func(pred) | ||
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| else: | ||
| for i in range(n_samples): | ||
| label,proba, distance = self.classifier.classify(list(X[i].toarray()[0])) | ||
| func(pred) | ||
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| return np.array(pred) | ||
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| def predict(self, X, y=None): | ||
| return self._timbl_predictions(X, part_index=0) | ||
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| def predict_proba(self, X, y=None): | ||
| """ | ||
| TIMBL is a discrete classifier. It cannot give probability estimations. | ||
| To ensure that scikit-learn functions with TIMBL (and especially metrics | ||
| such as ROC_AUC), this method is implemented. | ||
| For ROC_AUC, the classifier corresponds to a single point in ROC space, | ||
| instead of a probabilistic continuum such as classifiers that can give | ||
| a probability estimation (e.g. Linear classifiers). For an explanation, | ||
| see Fawcett (2005). | ||
| """ | ||
| return predict(X) | ||
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| def decision_function(self, X, y=None): | ||
| """ | ||
| The decision function is interpreted here as being the distance between | ||
| the instance that is being classified and the nearest point in k space. | ||
| """ | ||
| return self._timbl_predictions(X, part_index=1) | ||
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