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class SearchMood: |
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def __init__(self, mood_prompt, prior_init): |
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self.prior_init = prior_init |
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self.mood_prompt = mood_prompt |
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self.model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') |
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self.embeding = lambda mood_prompt, mood_state: (self.model.encode(mood_prompt, convert_to_tensor=True), self.model.encode(mood_state, convert_to_tensor=True)) |
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self.similar = lambda similarx, similary: util.pytorch_cos_sim(similarx, similary) |
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self.cx_sample = shelve.open('cx_sample.db')['sample'] |
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self.database = shelve.open('database.db') |
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SearchMood.prior_component = torch.tensor([0.,1.]) |
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self.prior_sample = torch.normal(self.prior_component[0], self.prior_component[1], size=(5,)) |
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self.sample_losses = None |
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def Hierarchical(self, data): |
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clusters, distances = hierarchical(data, distance='cosine', linkage='complete', return_clusters=True) |
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print(clusters) |
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def embedings(self, samplex, sampley): |
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emb = self.embeding(samplex, sampley) |
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embHx = self.Hierarchical(emb[0]) |
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embHy = self.Hierarchical(emb[1]) |
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similarity = self.similar(embHx, embHy) |
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return(similarity) |
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def mood_dist(self, data_sample=False, mood_prompt=False, search=True): |
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cx_index = [] |
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if search == True: |
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for mood_state in self.cx_sample: |
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index_sample = [] |
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max_sample = 0 |
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index_sample = 0 |
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for index, mood_prompts in enumerate(self.database['database']): |
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simemb = self.embedings(mood_state, mood_prompts) |
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if max_sample < simemb: |
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max_sample = simemb |
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index_sample = index |
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cx_index.append((float(index_sample))) |
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else: |
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cx_index.append(self.embedings(mood_prompt, data_sample)) |
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return(torch.tensor(cx_index)) |
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def loss_fn(self): |
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for sample in self.prior_sample: |
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sample = sample.item() |
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data_sample = self.database['database'][round(sample)] |
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samp_loss = self.mood_dist(data_sample, self.mood_prompt, search=False) |
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print(samp_loss) |
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if samp_loss.item() >= 1.: |
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print('test') |
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break |
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return(torch.tensor([samp_loss*-1])) |
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def search_compose(self): |
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for d in range(100): |
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optimizer = optim.Adagrad((self.prior_component[0], self.prior_component[1])) |
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optimizer.step(closure=self.loss_fn) |
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state_dict = optimizer.state_dict() |
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params = state_dict['param_groups'][0]['params'] |
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self.prior_component[0] = params[0] |
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self.prior_component[1] = params[1] |
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self.prior_sample = torch.normal(self.prior_component[0], self.prior_component[1], size=(5,)) |
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