Create score_caculator.py

Author: Prophet2023

DImmunScore/score_caculator.py

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+import pandas as pd
+import numpy as np
+import sklearn
+import os
+import copy
+from sklearn.metrics import roc_curve, auc
+from sklearn.metrics import accuracy_score, classification_report  
+
+
+
+def manhattan_distance2(array, dataframe):
+    # 检查参数是否合法
+    if not isinstance(array, np.ndarray) or not isinstance(dataframe, pd.DataFrame):
+        print("Invalid arguments. Please provide a numpy array and a pandas dataframe.")
+        return None
+    # 获取数组和Dataframe的维度
+    array_shape = array.shape
+    dataframe_shape = dataframe.shape
+    # 检查数组和Dataframe的维度是否匹配
+    if array_shape[0] != dataframe_shape[1]:
+        print("Dimension mismatch. The array and the dataframe must have the same number of rows.")
+        return None
+    # 创建一个空的Dataframe，用于存储结果
+    distance_arr = np.array([])
+
+    # 遍历Dataframe的每一列
+    for ind in dataframe.index:
+        # 计算数组和Dataframe的该列的曼哈顿距离
+        if dataframe.loc[ind]['y'] >= array[1]:
+            distance = 1 +  np.linalg.norm(array - dataframe.loc[ind].values, axis=0, ord=1) / np.sum(np.abs(array))
+        # elif  np.sum(np.abs(dataframe.loc[ind].values)) < np.sum(np.abs(array)):
+        else:
+            distance = 1 - np.linalg.norm(array - dataframe.loc[ind].values, axis=0, ord=1) / np.sum(np.abs(array))
+        # 将结果添加到结果Dataframe中
+        distance_arr = np.append(distance_arr, distance)
+    # 返回结果Dataframe
+    return distance_arr
+
+
+def immune_scror_calculate2(input_data_dict, features, refer, return_t='dict'):
+    if isinstance(input_data_dict, pd.DataFrame):
+        immune_score_df = copy.deepcopy(input_data_dict)
+        immune_score_df['Defense Immune Score'] = manhattan_distance2(array=refer[features].to_numpy(),
+                                                                   dataframe=immune_score_df[features])
+        return immune_score_df
+
+    elif isinstance(input_data_dict, dict):
+        input_data_df = copy.deepcopy(input_data_dict)
+
+        for k, v in input_data_df.items():
+            v["Disease Group"] = k
+            v['Defense Immune Score'] = manhattan_distance2(array=refer[features].to_numpy(),
+                                                           dataframe=v[features])
+        if return_t == 'dict':
+            immune_score_df = {k: v[['Disease Group', 'Defense Immune Score']] for k, v in input_data_df.items()}
+        else:
+            # 检查是否存在列 'Disease Group'
+            if 'Disease Group' in input_data_df.columns:
+                immune_score_df = input_data_df[['Disease Group', 'Defense Immune Score']]
+            else:
+                immune_score_df = input_data_df[['Defense Immune Score']]
+
+        return immune_score_df
+    else:
+        raise ValueError('to this error ')