Get Record(s) by Ids

本文介绍了一种通过解析Salesforce对象ID前缀来获取特定对象记录的方法,并提供了高效的批量查询实现,旨在优化SOQL查询性能。

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Use this to get record or records by id.

/**
 * Created by mengt on 2017/4/18.
 */

public class sobjectDaoHelper {
    public static Sobject getById(Id recordId){
        String queryStr = 'select ';
        String sObjectName = sobjectDaoHelper.findObjectNameFromRecordIdPrefix(recordId);
        if(sObjectName == ''|| sObjectName == null){ return null;}
        List<Schema.DescribeSObjectResult> sObjectResults = Schema.describeSObjects(new List<String>{sObjectName});
        if(sObjectResults == null || sObjectResults.size() == 0) {
            return null;
        }
        Schema.DescribeSObjectResult sObjectResult = sObjectResults.get(0);
        Map<String,SObjectField> maps = sObjectResult.fields.getMap();
        for(Schema.SObjectField objectField : maps.values()) {
            Schema.DescribeFieldResult fieldResult = objectField.getDescribe();
            if(fieldResult.isAccessible()) {
                queryStr += fieldResult.getName() + ',';
            }
        }
        queryStr = queryStr.substring(0,queryStr.length()-1);
        queryStr += ' from ' + sObjectName + ' where Id = :recordId';
        List<sObject> objects = Database.query(queryStr);
        if(objects == null || objects.size() == 0) {
            return null;
        }
        return objects.get(0);
    }
    /*
    可优化方案:循环查找perfixkey,可用map替换,在数据操作过大的时候计算时间会有所提高
    List<Id>限制 50000
    List对象限制 100
     */
    public static String findObjectNameFromRecordIdPrefix(String recordIdOrPrefix){
        String objectName = '';
        try{
            //Get prefix from record ID
            //This assumes that you have passed at least 3 characters
            String myIdPrefix = String.valueOf(recordIdOrPrefix).substring(0,3);

            //Get schema information
            Map<String, Schema.SObjectType> gd =  Schema.getGlobalDescribe();

            //Loop through all the sObject types returned by Schema
            for(Schema.SObjectType stype : gd.values()){
                Schema.DescribeSObjectResult r = stype.getDescribe();
                String prefix = r.getKeyPrefix();

                //Check if the prefix matches with requested prefix
                if(prefix!=null && prefix.equals(myIdPrefix)){
                    objectName = r.getName();
                    break;
                }
            }
        }catch(Exception e){
            System.debug(e);
        }
        return objectName;
    }

    public static Map<String,List<SObject>> getByIds(List<Id> recordIds){
        if(recordIds == null || recordIds.size()>50000){
            return null;
        }
        Map<String,List<Id>> mapIds = new Map<String,List<Id>>();
        for (Id thisid : recordIds) {
            String objectType = findObjectNameFromRecordIdPrefix(thisid);
            if(objectType == '' || objectType == null){ continue; }
            if(!mapIds.containsKey(objectType)){
                mapIds.put(objectType,new List<Id>());
            }
            mapIds.get(objectType).add(thisid);
        }
        if(mapIds.keySet().size() > 100){
            return null;
        }
        Map<String,List<SObject>> returnMap = new Map<String,List<SObject>>();
        List<String> objectNames = new List<String>();
        objectNames.addAll(mapIds.keySet());
        List<Schema.DescribeSObjectResult> sObjectResults = Schema.describeSObjects(objectNames);
        for (DescribeSObjectResult sObjectResult : sObjectResults) {
            String queryStr = 'select ';
            Map<String,SObjectField> maps = sObjectResult.fields.getMap();
            for(Schema.SObjectField objectField : maps.values()) {
                Schema.DescribeFieldResult fieldResult = objectField.getDescribe();
                if(fieldResult.isAccessible()) {
                    queryStr += fieldResult.getName() + ',';
                }
            }
            List<Id> queryIds = mapIds.get(sObjectResult.getName());
            queryStr = queryStr.substring(0,queryStr.length()-1);
            queryStr += ' from ' + sObjectResult.getName() + ' where Id = :queryIds';
            returnMap.put(sObjectResult.getName(),Database.query(queryStr));
        }
        return returnMap;
    }
}


下面这段代码是以中心recipe为每团reicpe的中心,其他的recipe对中心recipe做diff,然后训练每团各自的模型,这样数据太少,现在我想改成每团recipe两两进行差分,形成更多数据,然后拿来训练每团各自的模型:def construct_multi_base_differences_with_distance_vector( df: pd.DataFrame, feature_cols: List[str], target_col: str, recipe_id_col: str = “recipe_id”, base_ids: Optional[List[str]] = None, max_distance: float = 0.5, top_k: Optional[int] = None, weights: Optional[Dict[str, float]] = None ) -> pd.DataFrame: diffs = [] data = df[df[target_col].notna()].copy() X = data[feature_cols].copy() y = data[target_col].values ids = data[recipe_id_col].values scaler = MinMaxScaler() X_scaled = scaler.fit_transform(X) if weights is None: weights = {col: 1.0 for col in feature_cols} weight_vector = np.array([weights.get(col, 1.0) for col in feature_cols]) id_to_index = {rid: idx for idx, rid in enumerate(ids)} if base_ids is None: base_ids = list(ids) for base_id in base_ids: i = id_to_index.get(base_id) if i is None: continue base_vector = X_scaled[i] dist_vector = compute_weighted_l1_distance_vector(base_vector, X_scaled, weight_vector) valid_idxs = np.where((dist_vector > 0) & (dist_vector <= max_distance))[0] if top_k: valid_idxs = valid_idxs[np.argsort(dist_vector[valid_idxs])[:top_k]] for j in valid_idxs: delta_x = X.iloc[j].values - X.iloc[i].values delta_y = y[j] - y[i] record = { f"Δ{col}": delta_x[k] for k, col in enumerate(feature_cols) } record[f"Δ{target_col}"] = delta_y record["base_id"] = ids[i] record["compare_id"] = ids[j] record["distance"] = dist_vector[j] diffs.append(record) df_diff = pd.DataFrame(diffs) return df_diff def train_diff_models_by_base_no_shap( df_diff: pd.DataFrame, feature_cols: List[str], target_col: str, base_col: str = “base_id”, model_type: str = “lasso”, output_dir: str = “base_models” ) -> pd.DataFrame: os.makedirs(output_dir, exist_ok=True) base_ids = df_diff[base_col].unique() records = [] for base in base_ids: df_base = df_diff[df_diff[base_col] == base] if len(df_base) < 5: continue X = df_base[feature_cols].values y = df_base[target_col].values if model_type == "lasso": model = LassoCV(cv=3, random_state=42).fit(X, y) else: raise NotImplementedError("Only 'lasso' is implemented.") y_pred = model.predict(X) r2 = r2_score(y, y_pred) mse = mean_squared_error(y, y_pred) records.append({ "base_id": base, "r2": r2, "mse": mse, "n_samples": len(df_base), "coef_dict": dict(zip(feature_cols, model.coef_)) }) return pd.DataFrame(records)
最新发布
07-04
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