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main_single_score.py

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+"""
+    得到一个因子一个分数的记录列表
+"""
+
+import pickle
+import numpy as np
+from dataset_code.process_on_raw_data import form_raw_dataset, df_col_quchong
+from dataset_code.HMM_duoyinzi import solve2, form_model_dataset, form_model
+from public_tool.evaluate_plot import evaluate_plot
+import warnings
+warnings.filterwarnings("ignore")
+
+if __name__ == '__main__':
+
+    temp = pickle.load(open('save/classified by id/000001.XSHE.pkl', 'rb'))
+    temp = df_col_quchong(temp)
+    temp = [i for i in temp.columns]
+    feature_list = temp[temp.index('AccountsPayablesTDays'):]
+    score_record = np.zeros(len(feature_list))
+
+    for i in range(len(feature_list)):
+
+        now_feature = [feature_list[i]]
+
+        dataset, label, lengths, col_nan_record = form_raw_dataset(now_feature, label_length=3, verbose=False)
+
+        if len(label) == 0:
+            print('skip ' + now_feature[0])
+            continue
+
+        solved_dataset, allow_flag = solve2(dataset, now_feature, now_feature)
+
+        train_X, train_label, train_lengths = form_model_dataset(solved_dataset, label, allow_flag, lengths)
+
+        model = form_model(train_X, train_lengths, 3, 'diag', 1000, verbose=False)
+
+        score = evaluate_plot(model, train_X, train_label, train_lengths)
+        score_record[i] = score
+
+        print('all:%s, now:%s, ' % (len(feature_list), i + 1) + now_feature[0] + ': score:%s' % score)
+
+        pickle.dump([score_record, feature_list], open('save/duoyinzi_solve2_score.pkl', 'wb'))
+
+    pickle.dump([score_record, feature_list], open('save/duoyinzi_solve2_score.pkl', 'wb'))