95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
from models.learners import *
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from view_generators import VanillaFunGen
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from util.common import _normalize
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class DocEmbedderList:
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def __init__(self, embedder_list, probabilistic=True):
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"""
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Class that takes care of calling fit and transform function for every init embedder.
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:param embedder_list: list of embedders to be deployed
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:param probabilistic: whether to recast view generators output to vectors of posterior probabilities or not
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"""
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assert len(embedder_list) != 0, 'Embedder list cannot be empty!'
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self.embedders = embedder_list
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self.probabilistic = probabilistic
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if probabilistic:
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_tmp = []
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for embedder in self.embedders:
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if isinstance(embedder, VanillaFunGen):
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_tmp.append(embedder)
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else:
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_tmp.append(FeatureSet2Posteriors(embedder))
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self.embedders = _tmp
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def fit(self, lX, ly):
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for embedder in self.embedders:
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embedder.fit(lX, ly)
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return self
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def transform(self, lX):
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langs = sorted(lX.keys())
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lZparts = {lang: None for lang in langs}
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for embedder in self.embedders:
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lZ = embedder.transform(lX)
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for lang in langs:
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Z = lZ[lang]
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if lZparts[lang] is None:
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lZparts[lang] = Z
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else:
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lZparts[lang] += Z
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n_embedders = len(self.embedders)
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return {lang: lZparts[lang]/n_embedders for lang in langs}
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def fit_transform(self, lX, ly):
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return self.fit(lX, ly).transform(lX)
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class FeatureSet2Posteriors:
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def __init__(self, embedder, l2=True, n_jobs=-1):
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self.embedder = embedder
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self.l2 = l2
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self.n_jobs = n_jobs
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self.prob_classifier = MetaClassifier(
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SVC(kernel='rbf', gamma='auto', probability=True, cache_size=1000, random_state=1), n_jobs=n_jobs)
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def fit(self, lX, ly):
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lZ = self.embedder.fit_transform(lX, ly)
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self.prob_classifier.fit(lZ, ly)
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return self
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def transform(self, lX):
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lP = self.predict_proba(lX)
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lP = _normalize(lP, self.l2)
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return lP
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def fit_transform(self, lX, ly):
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return self.fit(lX, ly).transform(lX)
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def predict(self, lX):
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lZ = self.embedder.transform(lX)
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return self.prob_classifier.predict(lZ)
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def predict_proba(self, lX):
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lZ = self.embedder.transform(lX)
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return self.prob_classifier.predict_proba(lZ)
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class Funnelling:
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def __init__(self, first_tier: DocEmbedderList, meta_classifier: MetaClassifier, n_jobs=-1):
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self.first_tier = first_tier
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self.meta = meta_classifier
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self.n_jobs = n_jobs
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def fit(self, lX, ly):
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print('## Fitting first-tier learners!')
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lZ = self.first_tier.fit_transform(lX, ly)
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print('## Fitting meta-learner!')
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self.meta.fit(lZ, ly)
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def predict(self, lX):
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lZ = self.first_tier.transform(lX)
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ly = self.meta.predict(lZ)
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return ly
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