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How to use compose_feature method in lettuce-tools

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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