Best Python code snippet using lettuce-tools_python
mvFeatureExtractor.py
Source: mvFeatureExtractor.py
...22 elif (chart['chart_type'] == 'scatter'):23 return [0,1,0,0,0]24 elif (chart['chart_type'] == 'area'):25 return [1,0,0,0,0]26def compose_feature(chart, mv_charts):27 def if_decompose(c1, c2):28 return all(i in c2 for i in c1) ## c1 in c229 mv_charts = [c for c in mv_charts if chart['indices'] != c['indices'] and chart['chart_type'] != c['chart_type']]30 return sum(1 for c in mv_charts if if_decompose(chart, c))31def complementary_feature(chart, mv_charts):32 def if_complementary(pair):33 return len(set(itertools.chain(*pair))) == sum([len(x) for x in pair])34 mv_charts = [c for c in mv_charts if chart['indices'] != c['indices'] and chart['chart_type'] != c['chart_type']]35 combinations = [(chart, c) for c in mv_charts]36 return sum(1 for x in combinations if if_complementary(x))37def chart_to_feature(chart, mv_charts, all_charts_with_normed_score):38 chart_type_n_columns_feature = chart_type_feature(chart)39 score = [x for x in all_charts_with_normed_score if x['indices'] == chart['indices'] and x['chart_type'] == chart['chart_type']][0]['final_score']40 41 # # print(len(chart['indices']), chart_type_feature(chart), compose_feature(chart,mv_charts), 42 # # complementary_feature(chart,mv_charts), score)43 # print(chart)44 return [len(chart['indices']), *chart_type_feature(chart), compose_feature(chart,mv_charts), 45 complementary_feature(chart,mv_charts), score]46def charts_to_features_dl(mv_charts, all_charts_with_normed_score, seq_length = False):47 features = []48 for c in mv_charts:49 features.append(chart_to_feature(c, mv_charts, all_charts_with_normed_score))50 if seq_length != False:51 ## padding zero to match seq_length52 padding_zero = np.zeros((seq_length - len(features), len(features[0]))).tolist()53 features.extend(padding_zero)54 return features55def get_chart_lists(raw_lists):56 def get_chart(raw_chart):57 if raw_chart['markEncoding'] == 'arc':58 chart_type = 'pie'...
resampling.py
Source: resampling.py
1import numpy as np2import pandas as pd3from data_process.ProcessedData import ProcessedData4class ResamplingData(ProcessedData):5 def __init__(self, raw_data):6 super().__init__(raw_data)7 self.rest_columns = raw_data.rest_columns8 def process(self):9 equal_zero_index = (self.label_df != 1).values10 equal_one_index = ~equal_zero_index11 pass_feature = np.array(self.feature_df[equal_zero_index])12 fail_feature = np.array(self.feature_df[equal_one_index])13 diff_num = len(pass_feature) - len(fail_feature)14 if diff_num < 1 or len(fail_feature) <= 0:15 return16 temp_array = np.zeros([diff_num, len(self.feature_df.values[0])])17 for i in range(diff_num):18 temp_array[i] = fail_feature[i % len(fail_feature)]19 features_np = np.array(self.feature_df)20 compose_feature = np.vstack((features_np, temp_array))21 label_np = np.array(self.label_df)22 gen_label = np.ones(diff_num).reshape((-1, 1))23 compose_label = np.vstack((label_np, gen_label))24 self.label_df = pd.DataFrame(compose_label, columns=['error'], dtype=float)25 self.feature_df = pd.DataFrame(compose_feature, columns=self.feature_df.columns, dtype=float)...
CLIP_model.py
Source: CLIP_model.py
1import torch2import clip3import torch.nn as nn4class clip_model(nn.Module):5 def __init__(self, image_dim, text_dim):6 super().__init__()7 self.clip_model, _ = clip.load("ViT-B/32")8 # self.compose_linear = nn.Linear(image_dim+text_dim, image_dim)9 def forward(self, image, text):10 image_feature = self.clip_model.encode_image(image) # batch x 51211 text_feature = self.clip_model.encode_text(clip.tokenize(text).cuda()) # batch x 51212 compose_feature = torch.cat((image_feature, text_feature), dim=-1)13 # compose_feature = self.compose_linear(compose_feature)...
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