The introduction of AutoSmart

The 1st place solution for KDD Cup 2019 AutoML Track

How to install

pip install AutoSmart

How to use

import auto_smart

info = auto_smart.read_info("data")
train_data,train_label = auto_smart.read_train("data",info)
test_data = auto_smart.read_test("data",info)
auto_smart.train_and_predict(train_data,train_label,info,test_data)

Data Sample

Data

This page describes the datasets that our system can deal with.

Components

Each dataset is split into two subsets, namely the training set and the testing set.

Both sets have:

a main table file that stores the main table (label excluded);
multiple related table files that store the related tables;
an info dictionary that contains important information about the dataset, including table relations;
The training set has an additional label file that stores labels associated with the main table.

Table files

Each table file is a CSV file that stores a table (main or related), with '\t' as the delimiter. The first row indicates the names of features, a.k.a 'schema', and the following rows are the records.

The type of each feature can be found in the info dictionary that will be introduced soon.

There are 4 types of features, indicated by "cat", "num", "multi-cat", and "time", respectively:

cat: categorical feature, an integer
num: numerical Feature, a real value.
multi-cat: multi-value categorical Feature: a set of integers, split by the comma. The size of the set is not fixed and can be different for each instance. For example, topics of an article, words in a title, items bought by a user and so on.
time: time feature, an integer that indicates the timestamp.

Label file

The label file is associated only with the main table in the training set. It is a CSV file that contains only one column, with the first row as the header and the remaining indicating labels associated with instances in the main table.

info dictionary

Important information about each dataset is stored in a python dictionary structure named as info, which acts as an input of this system. Generally,you need to manually generate the dictionary information info.json file. Here we give details about info.

Descriptions of the keys in info:

time_budget: time budget for this dataset (sec).
time_col: the column name of the primary timestamp; Each dataset has one unique time_col; time_col is definitely contained in the main table, but not necessarily in a related table;
start_time: DEPRECATED.
tables: a dictionary that stores information about tables. Each key indicates a table, and its corresponding value is a dictionary that indicates the type of each column in this table. Two kinds of keys are contained in tables:
- main: the main table;
- table_{i}: the i-th related table.
- There are 4 types of features, indicated by "cat", "num", "multi-cat", and "time", respectively:
  - cat: categorical feature, an integer
  - num: numerical Feature, a real value.
  - multi-cat: multi-value categorical Feature: a set of integers, split by the comma. The size of the set is not fixed and can be different for each instance. For example, topics of an article, words in a title, items bought by a user and so on.
  - time: time feature, an integer that indicates the timestamp.
relations: a list that stores table relations in the dataset. Each relation can be represented as an ordered table pair (table_A, table_B), a key column key that appears in both tables and acts as the pivot of table joining, and a relation type type. Different relation types will be introduced shortly.

Relations Between Tables

Four table relations are considered in this system:

one-to-one (1-1): the key columns in both table_A and table_B have no duplicated values;
one-to-many (1-M): the key column in table_A has no duplicated values, but that in table_B may have duplicated values;
many-to-one (M-1): the key column in table_A may have duplicated values, but that in table_B has no duplicated values;
many-to-many (M-M): the key columns in both table_A and table_B may have duplicated values.

Contact Us

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