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tech.ml.dataset Walkthrough
Let's take a moment to walkthrough the tech.ml.dataset system. This system was built
over the course of a few months in order to make working with columnar data easier
in the same manner as one would work with data.table in R or pandas in Python. While
it takes design inspiration from these sources it does not strive to be a copy in any
way but rather an extension to the core Clojure language that is built for good
performance when processing datasets of realistic sizes which in our case means
millions of rows and tens of columns.
High Level Design
Logically, a dataset is a map of column name to column data. Column data is typed so for instance you may have a column of 16 bit integers or 64 bit floating point numbers. Column names may be any java object and column values may be of the tech.datatype primitive, datetime, or objects. Data is stored contiguously in jvm arrays while missing values are indicated with bitsets.
Given this definition, the intention is to allow more or less normal flows familiar to most Clojure programmers:
- Dataset creation in the form of csv,tsv (and gzipped varieties of these), maps of column name to column values, and arbitrary sequences of maps.
- Pretty printing of datasets and, to a lesser extent, columns. Simple selection of a given column and various functions describing the details of a column.
- Access to the values in a column including eliding or erroring on missing values.
- Select subrect of dataset defined by a sequence of columns and some sequence of indexes.
sort-by,filter,group-byare modified operations that operate on a logical sequence of maps and an arbitrary function but return a new dataset.- Efficient elementwise operations such as linear combinations of columns.
- Statistical and ml-based analysis of some subset of columns either on their own
or as they relate to another
targetcolumn. - Conversion of the dataset to sequences of maps, sequences of persistent vectors, and rowwise sequences of java arrays of a chosen primitive datatype.
Dataset Creation
->dataset ->>dataset
Dataset creation can happen in many ways. For data in csv, tsv, or sequence of maps
format there are two functions that differ in where the data is passed in, ->dataset
and ->>dataset. These functions several arguments:
- A
StringorInputStreamwill be interpreted as a file (or gzipped file if it ends with .gz) of tsv or csv data. The system will attempt to autodetect if this is csv or tsv and then has some extensive engineering put into column datatype detection mechanisms which can be overridden. - A sequence of maps may be passed in in which case the first N maps are scanned in order to derive the column datatypes before the actual columns are created.
user> (require '[tech.ml.dataset :as ds])
nil
user> (require '[tech.ml.dataset.column :as ds-col])
nil
user> (ds/->dataset [{:a 1 :b 2} {:a 2 :c 3}])
_unnamed [2 3]:
| :a | :b | :c |
|----+--------+--------|
| 1 | 2 | -32768 |
| 2 | -32768 | 3 |
CSV/TSV/MAPSEQ/XLS/XLSX Parsing Options
It is important to note that there are several options for parsing files. A few important ones are column whitelist/blacklists, num records, and ways to specify exactly how to parse the string data:
user> (doc ds/->dataset)
-------------------------
tech.ml.dataset/->dataset
([dataset {:keys [table-name dataset-name], :as options}] [dataset])
Create a dataset from either csv/tsv or a sequence of maps.
* A `String` or `InputStream` will be interpreted as a file (or gzipped file if it
ends with .gz) of tsv or csv data. The system will attempt to autodetect if this
is csv or tsv and then engineering around detecting datatypes all of which can
be overridden.
* A sequence of maps may be passed in in which case the first N maps are scanned in
order to derive the column datatypes before the actual columns are created.
Options:
:table-name - set the name of the dataset (deprecated in favor of :dataset-name).
:dataset-name - set the name of the dataset.
:column-whitelist - either sequence of string column names or sequence of column
indices of columns to whitelist.
:column-blacklist - either sequence of string column names or sequence of column
indices of columns to blacklist.
:num-rows - Number of rows to read
:header-row? - Defaults to true, indicates the first row is a header.
:key-fn - function to be applied to column names. Typical use is:
`:key-fn keyword`.
:separator - Add a character separator to the list of separators to auto-detect.
:csv-parser - Implementation of univocity's AbstractParser to use. If not provided
a default permissive parser is used. This way you parse anything that univocity
supports (so flat files and such).
:skip-bad-rows? - For really bad files, some rows will not have the right column
counts for all rows. This skips rows that fail this test.
:max-chars-per-column - Defaults to 4096. Columns with more characters that this
will result in an exception.
:parser-fn -
- keyword - all columns parsed to this datatype
- ifn? - called with two arguments: (parser-fn column-name-or-idx column-data)
- Return value must be implement tech.ml.dataset.parser.PColumnParser in
which case that is used or can return nil in which case the default
column parser is used.
- tuple - pair of [datatype parse-fn] in which case container of type [datatype]
will be created.
parse-fn can be one of:
:relaxed? - data will be parsed such that parse failures of the standard
parse functions do not stop the parsing process. :unparsed-values and
:unparsed-indexes are available in the metadata of the column that tell
you the values that failed to parse and their respective indexes.
fn? - function from str-> one of #{:missing :parse-failure value}.
Exceptions here always kill the parse process. :missing will get marked
in the missing indexes, and :parse-failure will result in the index being
added to missing, the unparsed the column's :unparsed-values and
:unparsed-indexes will be updated.
string? - for datetime types, this will turned into a DateTimeFormatter via
DateTimeFormatter/ofPattern.
DateTimeFormatter - use with the appropriate temporal parse static function
to parse the value.
- map - the header-name-or-idx is used to lookup value. If not nil, then
value can be any of the above options. Else the default column parser
is used.
:parser-scan-len - Length of initial column data used for parser-fn's datatype
detection routine. Defaults to 100.
Returns a new dataset
nil
user> (ds/->dataset "data/ames-house-prices/train.csv"
{:column-whitelist ["SalePrice" "1stFlrSF" "2ndFlrSF"]
:n-records 5})
data/ames-house-prices/train.csv [4 3]:
| SalePrice | 1stFlrSF | 2ndFlrSF |
|-----------+----------+----------|
| 208500 | 856 | 854 |
| 181500 | 1262 | 0 |
| 223500 | 920 | 866 |
| 140000 | 961 | 756 |
user> (ds/->dataset "data/ames-house-prices/train.csv"
{:column-whitelist ["SalePrice" "1stFlrSF" "2ndFlrSF"]
:n-records 5
:parser-fn :float32})
data/ames-house-prices/train.csv [4 3]:
| SalePrice | 1stFlrSF | 2ndFlrSF |
|------------+----------+----------|
| 208500.000 | 856.000 | 854.000 |
| 181500.000 | 1262.000 | 0.000 |
| 223500.000 | 920.000 | 866.000 |
| 140000.000 | 961.000 | 756.000 |
user> (ds/->dataset "data/ames-house-prices/train.csv"
{:column-whitelist ["SalePrice" "1stFlrSF" "2ndFlrSF"]
:n-records 5
:parser-fn {"SalePrice" :float32}})
data/ames-house-prices/train.csv [4 3]:
| SalePrice | 1stFlrSF | 2ndFlrSF |
|------------+----------+----------|
| 208500.000 | 856 | 854 |
| 181500.000 | 1262 | 0 |
| 223500.000 | 920 | 866 |
| 140000.000 | 961 | 756 |
You can also supply a tuple of [datatype parse-fn] if you have a specific
datatype and parse function you want to use. For datetime types parse-fn
can additionally be a DateTimeFormat format string or a DateTimeFormat object:
nil
user> (def data (ds/select (ds/->dataset "test/data/file_example_XLSX_1000.xlsx")
:all (range 5)))
#'user/data
user> data
Sheet1 [5 8]:
| 0 | First Name | Last Name | Gender | Country | Age | Date | Id |
|-------+------------+-----------+--------+---------------+--------+------------+----------|
| 1.000 | Dulce | Abril | Female | United States | 32.000 | 15/10/2017 | 1562.000 |
| 2.000 | Mara | Hashimoto | Female | Great Britain | 25.000 | 16/08/2016 | 1582.000 |
| 3.000 | Philip | Gent | Male | France | 36.000 | 21/05/2015 | 2587.000 |
| 4.000 | Kathleen | Hanner | Female | United States | 25.000 | 15/10/2017 | 3549.000 |
| 5.000 | Nereida | Magwood | Female | United States | 58.000 | 16/08/2016 | 2468.000 |
user> ;; Note the Date actually didn't parse out because it is dd/MM/yyyy format:
user> (dtype/get-datatype (data "Date"))
:string
user> (def data (ds/select (ds/->dataset "test/data/file_example_XLSX_1000.xlsx"
{:parser-fn {"Date" [:local-date "dd/MM/yyyy"]}})
:all (range 5)))
#'user/data
user> data
Sheet1 [5 8]:
| 0 | First Name | Last Name | Gender | Country | Age | Date | Id |
|-------+------------+-----------+--------+---------------+--------+------------+----------|
| 1.000 | Dulce | Abril | Female | United States | 32.000 | 2017-10-15 | 1562.000 |
| 2.000 | Mara | Hashimoto | Female | Great Britain | 25.000 | 2016-08-16 | 1582.000 |
| 3.000 | Philip | Gent | Male | France | 36.000 | 2015-05-21 | 2587.000 |
| 4.000 | Kathleen | Hanner | Female | United States | 25.000 | 2017-10-15 | 3549.000 |
| 5.000 | Nereida | Magwood | Female | United States | 58.000 | 2016-08-16 | 2468.000 |
user> (dtype/get-datatype (data "Date"))
:local-date
user> (def data (ds/select (ds/->dataset "test/data/file_example_XLSX_1000.xlsx"
{:parser-fn {"Date" [:local-date "dd/MM/yyyy"]
"Id" :int32
0 :int32
"Age" :int16}})
:all (range 5)))
#'user/data
user> data
Sheet1 [5 8]:
| 0 | First Name | Last Name | Gender | Country | Age | Date | Id |
|---+------------+-----------+--------+---------------+-----+------------+------|
| 1 | Dulce | Abril | Female | United States | 32 | 2017-10-15 | 1562 |
| 2 | Mara | Hashimoto | Female | Great Britain | 25 | 2016-08-16 | 1582 |
| 3 | Philip | Gent | Male | France | 36 | 2015-05-21 | 2587 |
| 4 | Kathleen | Hanner | Female | United States | 25 | 2017-10-15 | 3549 |
| 5 | Nereida | Magwood | Female | United States | 58 | 2016-08-16 | 2468 |
A reference to what is possible is in parse-test.
name-value-seq->dataset
Given a map of name->column data produce a new dataset. If column data is untyped (like a persistent vector) then the column datatype is either string or double, dependent upon the first entry of the column data sequence.
If the column data is one of the object numeric primitive types, so
Float as opposed to float, then missing elements will be marked as
missing and the default empty-value will be used in the primitive storage.
user> (ds/name-values-seq->dataset {:age [1 2 3 4 5]
:name ["a" "b" "c" "d" "e"]})
_unnamed [5 2]:
| :age | :name |
|-------+-------|
| 1.000 | a |
| 2.000 | b |
| 3.000 | c |
| 4.000 | d |
| 5.000 | e |
Basic Dataset Manipulation
Dataset are logically maps when treated like functions and sequences of columns when treated like sequences.
user> (def new-ds (ds/->dataset [{:a 1 :b 2} {:a 2 :c 3}]))
#'user/new-ds
user> (first new-ds)
#tech.ml.dataset.column<int16>[2]
:a
[1, 2, ]
user> (new-ds :c)
#tech.ml.dataset.column<int16>[2]
:c
[-32768, 3, ]
[]
user> (ds-col/missing (new-ds :b))
[1]
user> (ds-col/missing (new-ds :c))
[0]
user> (first new-ds)
#tech.ml.dataset.column<int16>[2]
:a
[1, 2, ]
It is safe to print out very large columns. The system will only print out the first 20 or values. In this way it can be useful to get a feel for the data in a particular column.
Access To Column Values
Columns are convertible (at least) to tech.datatype readers. These derive from java.util.List and as such allow efficient iteration and bulk copy to other datastructures.
user> (ds/name-values-seq->dataset {:age [1 2 3 4 5]
:name ["a" "b" "c" "d" "e"]})
_unnamed [5 2]:
| :age | :name |
|-------+-------|
| 1.000 | a |
| 2.000 | b |
| 3.000 | c |
| 4.000 | d |
| 5.000 | e |
user> (def nameage *1)
#'user/nameage
user> (require '[tech.v2.datatype :as dtype])
nil
user> (dtype/->reader (nameage :age))
[1.0 2.0 3.0 4.0 5.0]
user> (dtype/->reader (nameage :name))
["a" "b" "c" "d" "e"]
user> (dtype/->array-copy (nameage :age))
[1.0, 2.0, 3.0, 4.0, 5.0]
user> (type *1)
[D
user> (def col-reader (dtype/->reader (nameage :age)))
#'user/col-reader
user> (col-reader 0)
1.0
user> (col-reader 1)
2.0
user> (col-reader 2)
3.0
In the same vein, you can access entire rows of the dataset as a reader that converts the data either into a persistent vector in the same column-order as the dataset or a sequence of maps with each entry named. This type of conversion does not include any mapping to or from labelled values so as such represented the dataset as it is stored in memory:
user> (ds/value-reader nameage)
[[1.0 "a"] [2.0 "b"] [3.0 "c"] [4.0 "d"] [5.0 "e"]]
user> (ds/mapseq-reader nameage)
[{:age 1.0, :name "a"} {:age 2.0, :name "b"} {:age 3.0, :name "c"} {:age 4.0, :name "d"} {:age 5.0, :name "e"}]
Subrect Selection
The dataset system offers two methods to select subrects of information from the dataset. This results in a new dataset.
(def ames-ds (ds/->dataset "file://data/ames-house-prices/train.csv.gz"))
#'user/ames-ds
user> (ds/column-names ames-ds)
("Id"
"MSSubClass"
"MSZoning"
"LotFrontage"
...)
user> (ames-ds "KitchenQual")
#tech.ml.dataset.column<string>[1460]
KitchenQual
[Gd, TA, Gd, Gd, Gd, TA, Gd, TA, TA, TA, TA, Ex, TA, Gd, TA, TA, TA, TA, Gd, TA, ...]
user> (ames-ds "SalePrice")
#tech.ml.dataset.column<int32>[1460]
SalePrice
[208500, 181500, 223500, 140000, 250000, 143000, 307000, 200000, 129900, 118000, 129500, 345000, 144000, 279500, 157000, 132000, 149000, 90000, 159000, 139000, ...]
user> (ds/select ames-ds ["KitchenQual" "SalePrice"] [1 3 5 7 9])
[5 2]:
| KitchenQual | SalePrice |
|-------------+-----------|
| TA | 181500 |
| Gd | 140000 |
| TA | 143000 |
| TA | 200000 |
| TA | 118000 |
user> (ds/select-columns ames-ds ["KitchenQual" "SalePrice"])
[1460 2]:
| KitchenQual | SalePrice |
|-------------+-----------|
| Gd | 208500 |
| TA | 181500 |
| Gd | 223500 |
| Gd | 140000 |
| Gd | 250000 |
| TA | 143000 |
| Gd | 307000 |
| TA | 200000 |
| TA | 129900 |
...
Add, Remove, Update
user> (require '[tech.v2.datatype.functional :as dfn])
nil
;;Log doesn't work if the incoming value isn't a float32 or a float64. SalePrice is
;;of datatype :int32 so we convert it before going into log.
user> (ds/update-column small-ames "SalePrice" #(-> (dtype/->reader % :float64)
dfn/log))
[5 2]:
| KitchenQual | SalePrice |
|-------------+-----------|
| TA | 12.109 |
| Gd | 11.849 |
| TA | 11.871 |
| TA | 12.206 |
| TA | 11.678 |
user> (ds/add-or-update-column small-ames "Range" (float-array (range 5)))
[5 3]:
| KitchenQual | SalePrice | Range |
|-------------+-----------+-------|
| TA | 181500 | 0.000 |
| Gd | 140000 | 1.000 |
| TA | 143000 | 2.000 |
| TA | 200000 | 3.000 |
| TA | 118000 | 4.000 |
user> (ds/remove-column small-ames "KitchenQual")
[5 1]:
| SalePrice |
|-----------|
| 181500 |
| 140000 |
| 143000 |
| 200000 |
| 118000 |
Sort-by, Filter, Group-by
These are prefixed by ds to differentiate them from the base clojure versions but
they do conceptually the same thing with the exception that they return new datasets
as opposed to sequences. The predicate/key-fn used by these functions are passed
sequences of maps but if you know you want to filter/sort-by/group-by a single
column it is more efficient to use the -column versions of these functions.
In the case of ds-group-by-column you then get a map of column value to
dataset container rows that had that column value.
user> (-> (ds/filter #(< 30000 (get % "SalePrice")) ames-ds)
(ds/select ["SalePrice" "KitchenQual"] (range 5)))
[5 2]:
| SalePrice | KitchenQual |
|-----------+-------------|
| 208500 | Gd |
| 181500 | TA |
| 223500 | Gd |
| 140000 | Gd |
| 250000 | Gd |
user> (-> (ds/sort-by #(get % "SalePrice") ames-ds)
(ds/select ["SalePrice" "KitchenQual"] (range 5)))
[5 2]:
| SalePrice | KitchenQual |
|-----------+-------------|
| 34900 | TA |
| 35311 | TA |
| 37900 | TA |
| 39300 | Fa |
| 40000 | TA |
user> (def group-map (->> (ds/select ames-ds ["SalePrice" "KitchenQual"] (range 20))
(ds/group-by #(get % "KitchenQual"))))
#'user/group-map
user> (keys group-map)
("Gd" "TA" "Ex")
user> (first group-map)
["Gd" [7 2]:
| SalePrice | KitchenQual |
|-----------+-------------|
| 208500 | Gd |
| 223500 | Gd |
| 140000 | Gd |
| 250000 | Gd |
| 307000 | Gd |
| 279500 | Gd |
| 159000 | Gd |
]
user> (def group-map (->> (ds/select ames-ds ["SalePrice" "KitchenQual"] (range 20))
(ds/group-by-column "KitchenQual")))
#'user/group-map
user> (keys group-map)
("Gd" "TA" "Ex")
user> (first group-map)
["Gd" Gd [7 2]:
| SalePrice | KitchenQual |
|-----------+-------------|
| 208500 | Gd |
| 223500 | Gd |
| 140000 | Gd |
| 250000 | Gd |
| 307000 | Gd |
| 279500 | Gd |
| 159000 | Gd |
]
Combining a group-by variant with descriptive-stats can quickly help break down
a dataset as it relates to a categorical value:
user> (->> (ds/select-columns ames-ds ["SalePrice" "KitchenQual" "BsmtFinSF1" "GarageArea"])
(ds/group-by-column "KitchenQual")
(map (fn [[k v-ds]]
(-> (ds/descriptive-stats v-ds)
(ds/set-dataset-name k)))))
(Gd [4 10]:
| :col-name | :datatype | :n-valid | :n-missing | :mean | :mode | :min | :max | :standard-deviation | :skew |
|-------------+-----------+----------+------------+------------+-------+-----------+------------+---------------------+-------|
| BsmtFinSF1 | int32 | 586 | 0 | 456.469 | | 0.000 | 1810.000 | 455.209 | 0.597 |
| GarageArea | int32 | 586 | 0 | 549.101 | | 0.000 | 1069.000 | 174.387 | 0.227 |
| KitchenQual | string | 586 | 0 | NaN | Gd | NaN | NaN | NaN | NaN |
| SalePrice | int32 | 586 | 0 | 212116.031 | | 79000.000 | 625000.000 | 64020.176 | 1.189 |
TA [4 10]:
| :col-name | :datatype | :n-valid | :n-missing | :mean | :mode | :min | :max | :standard-deviation | :skew |
|-------------+-----------+----------+------------+------------+-------+-----------+------------+---------------------+-------|
| BsmtFinSF1 | int32 | 735 | 0 | 394.337 | | 0.000 | 1880.000 | 360.215 | 0.628 |
| GarageArea | int32 | 735 | 0 | 394.241 | | 0.000 | 1356.000 | 187.557 | 0.175 |
| KitchenQual | string | 735 | 0 | NaN | TA | NaN | NaN | NaN | NaN |
| SalePrice | int32 | 735 | 0 | 139962.516 | | 34900.000 | 375000.000 | 38896.281 | 0.999 |
Ex [4 10]:
| :col-name | :datatype | :n-valid | :n-missing | :mean | :mode | :min | :max | :standard-deviation | :skew |
|-------------+-----------+----------+------------+------------+-------+-----------+------------+---------------------+--------|
| BsmtFinSF1 | int32 | 100 | 0 | 850.610 | | 0.000 | 5644.000 | 799.383 | 2.144 |
| GarageArea | int32 | 100 | 0 | 706.430 | | 0.000 | 1418.000 | 236.293 | -0.187 |
| KitchenQual | string | 100 | 0 | NaN | Ex | NaN | NaN | NaN | NaN |
| SalePrice | int32 | 100 | 0 | 328554.656 | | 86000.000 | 755000.000 | 120862.945 | 0.937 |
Fa [4 10]:
| :col-name | :datatype | :n-valid | :n-missing | :mean | :mode | :min | :max | :standard-deviation | :skew |
|-------------+-----------+----------+------------+------------+-------+-----------+------------+---------------------+-------|
| BsmtFinSF1 | int32 | 39 | 0 | 136.513 | | 0.000 | 932.000 | 209.117 | 1.975 |
| GarageArea | int32 | 39 | 0 | 214.564 | | 0.000 | 672.000 | 201.934 | 0.423 |
| KitchenQual | string | 39 | 0 | NaN | Fa | NaN | NaN | NaN | NaN |
| SalePrice | int32 | 39 | 0 | 105565.203 | | 39300.000 | 200000.000 | 36004.254 | 0.242 |
)
Descriptive Stats And GroupBy And DateTime Types
This is best illustrated by an example:
user> (def stocks (ds/->dataset "test/data/stocks.csv"))
#'user/stocks
user> (ds/select stocks :all (range 5))
test/data/stocks.csv [5 3]:
| symbol | date | price |
|--------+------------+--------|
| MSFT | 2000-01-01 | 39.810 |
| MSFT | 2000-02-01 | 36.350 |
| MSFT | 2000-03-01 | 43.220 |
| MSFT | 2000-04-01 | 28.370 |
| MSFT | 2000-05-01 | 25.450 |
user> (->> (ds/group-by-column "symbol" stocks)
(map (fn [[k v]] (ds/descriptive-stats v))))
(MSFT: descriptive-stats [3 10]:
| :col-name | :datatype | :n-valid | :n-missing | :mean | :mode | :min | :max | :standard-deviation | :skew |
|-----------+--------------------+----------+------------+------------+-------+------------+------------+---------------------+-------|
| date | :packed-local-date | 123 | 0 | 2005-01-30 | | 1999-12-31 | 2010-02-28 | NaN | NaN |
| price | :float32 | 123 | 0 | 24.737 | | 15.810 | 43.220 | 4.304 | 1.166 |
| symbol | :string | 123 | 0 | | MSFT | | | NaN | NaN |
GOOG: descriptive-stats [3 10]:
| :col-name | :datatype | :n-valid | :n-missing | :mean | :mode | :min | :max | :standard-deviation | :skew |
|-----------+--------------------+----------+------------+------------+-------+------------+------------+---------------------+--------|
| date | :packed-local-date | 68 | 0 | 2007-05-17 | | 2004-08-01 | 2010-02-28 | NaN | NaN |
| price | :float32 | 68 | 0 | 415.870 | | 102.370 | 707.000 | 135.070 | -0.228 |
| symbol | :string | 68 | 0 | | GOOG | | | NaN | NaN |
AAPL: descriptive-stats [3 10]:
| :col-name | :datatype | :n-valid | :n-missing | :mean | :mode | :min | :max | :standard-deviation | :skew |
|-----------+--------------------+----------+------------+------------+-------+------------+------------+---------------------+-------|
| date | :packed-local-date | 123 | 0 | 2005-01-30 | | 1999-12-31 | 2010-02-28 | NaN | NaN |
| price | :float32 | 123 | 0 | 64.730 | | 7.070 | 223.020 | 63.124 | 0.932 |
| symbol | :string | 123 | 0 | | AAPL | | | NaN | NaN |
IBM: descriptive-stats [3 10]:
| :col-name | :datatype | :n-valid | :n-missing | :mean | :mode | :min | :max | :standard-deviation | :skew |
|-----------+--------------------+----------+------------+------------+-------+------------+------------+---------------------+-------|
| date | :packed-local-date | 123 | 0 | 2005-01-30 | | 1999-12-31 | 2010-02-28 | NaN | NaN |
| price | :float32 | 123 | 0 | 91.261 | | 53.010 | 130.320 | 16.513 | 0.444 |
| symbol | :string | 123 | 0 | | IBM | | | NaN | NaN |
AMZN: descriptive-stats [3 10]:
| :col-name | :datatype | :n-valid | :n-missing | :mean | :mode | :min | :max | :standard-deviation | :skew |
|-----------+--------------------+----------+------------+------------+-------+------------+------------+---------------------+-------|
| date | :packed-local-date | 123 | 0 | 2005-01-30 | | 1999-12-31 | 2010-02-28 | NaN | NaN |
| price | :float32 | 123 | 0 | 47.987 | | 5.970 | 135.910 | 28.891 | 0.982 |
| symbol | :string | 123 | 0 | | AMZN | | | NaN | NaN |
)
Elementwise Operations
Anything convertible to a reader such as persisent vectors or anything deriving from
both java.util.List and java.util.RandomAccess can be converted to a reader of
any datatype. Columns are exactly this so we can add a new column to the dataset
that is a linear combination of other columns using add-or-update-column:
user> (require '[tech.v2.datatype.functional :as dfn])
nil
user> (def updated-ames
(ds/add-or-update-column ames-ds
"TotalBath"
(dfn/+ (ames-ds "BsmtFullBath")
(dfn/* 0.5 (ames-ds "BsmtHalfBath"))
(ames-ds "FullBath")
(dfn/* 0.5 (ames-ds "HalfBath")))))
#'user/updated-ames
user> (updated-ames "TotalBath")
#tech.ml.dataset.column<float64>[1460]
TotalBath
[3.500, 2.500, 3.500, 2.000, 3.500, 2.500, 3.000, 3.500, 2.000, 2.000, 2.000, 4.000, 2.000, 2.000, 2.500, 1.000, 2.000, 2.000, 2.500, 1.000, ...]
We can also implement a completely dynamic operation to create a new column by implementing the appropriate reader interface from the datatype library:
user> (import '[tech.v2.datatype ObjectReader])
tech.v2.datatype.ObjectReader
user> (require '[tech.v2.datatype.typecast :as typecast])
nil
user> (def named-baths
(ds/add-or-update-column
updated-ames
"NamedBaths"
;;Type out total baths so we know the datatype we are dealing with
(let [total-baths (typecast/datatype->reader
:float64 (updated-ames "TotalBath"))]
(reify ObjectReader
;;Since this is an object reader, we have to specify
;;string as the datatype.
(getDatatype [rdr] :string)
(lsize [rdr] (.lsize total-baths))
(read [rdr idx]
(let [tbaths (.read total-baths idx)]
(cond
(< tbaths 1.0)
"almost none"
(< tbaths 2.0)
"somewhat doable"
(< tbaths 3.0)
"getting somewhere"
:else
"living in style")))))))
#'user/named-baths
user> (named-baths "NamedBaths")
#tech.ml.dataset.column<string>[1460]
NamedBaths
[living in style, getting somewhere, living in style, getting somewhere, living in style, getting somewhere, living in style, living in style, getting somewhere, getting somewhere, getting somewhere, living in style, getting somewhere, getting somewhere, getting somewhere, somewhat doable, getting somewhere, getting somewhere, getting somewhere, somewhat doable, ...]
;; Here we see that the higher level houses all have more bathrooms
user> (def sorted-named-baths (ds/ds-sort-by-column "SalePrice" > named-baths))
#'user/sorted-named-baths
user> (sorted-named-baths "NamedBaths")
#tech.ml.dataset.column<string>[1460]
NamedBaths
[living in style, living in style, living in style, living in style, living in style, living in style, living in style, living in style, living in style, living in style, getting somewhere, living in style, living in style, living in style, living in style, living in style, living in style, living in style, living in style, living in style, ...]
user> (->> (sorted-named-baths "NamedBaths")
(dtype/->reader)
(take-last 10))
("somewhat doable"
"getting somewhere"
"somewhat doable"
"somewhat doable"
"somewhat doable"
"somewhat doable"
"somewhat doable"
"somewhat doable"
"somewhat doable"
"somewhat doable")
DateTime Types
Brand new. Experimental. all that stuff.
Support for reading datetime types and manipulating them. Please checkout the
tech.datatype datetime documentation for using this feature.
user> (def stocks (ds/->dataset "test/data/stocks.csv"))
#'user/stocks
user> (ds/head stocks)
test/data/stocks.csv [5 3]:
| :symbol | :date | :price |
|---------+------------+--------|
| MSFT | 2000-01-01 | 39.81 |
| MSFT | 2000-02-01 | 36.35 |
| MSFT | 2000-03-01 | 43.22 |
| MSFT | 2000-04-01 | 28.37 |
| MSFT | 2000-05-01 | 25.45 |
user> (dtype/get-datatype (stocks :date))
:packed-local-date
user> (require '[tech.v2.datatype.datetime.operations :as dtype-dt-ops])
nil
user> (ds/head (ds/update-column stocks :date dtype-dt-ops/get-epoch-milliseconds))
test/data/stocks.csv [5 3]:
| :symbol | :date | :price |
|---------+----------------------+--------|
| MSFT | 2000-01-01T00:00:00Z | 39.81 |
| MSFT | 2000-02-01T00:00:00Z | 36.35 |
| MSFT | 2000-03-01T00:00:00Z | 43.22 |
| MSFT | 2000-04-01T00:00:00Z | 28.37 |
| MSFT | 2000-05-01T00:00:00Z | 25.45 |
;;How about the yearly averages by symbol of the stocks
user> (require '[tech.v2.datatype.functional :as dfn])
nil
user> (->> (ds/add-or-update-column stocks :years (dtype-dt-ops/get-years (stocks :date)))
(ds/group-by (juxt :symbol :years))
(vals)
;;stream is a sequence of datasets at this point.
(map (fn [ds]
{:symbol (first (ds :symbol))
:years (first (ds :years))
:avg-price (dfn/mean (ds :price))}))
(sort-by (juxt :symbol :years))
(ds/->>dataset)
(ds/head 10))
_unnamed [10 3]:
| :symbol | :years | :avg-price |
|---------+--------+------------|
| AAPL | 2000 | 21.75 |
| AAPL | 2001 | 10.18 |
| AAPL | 2002 | 9.408 |
| AAPL | 2003 | 9.347 |
| AAPL | 2004 | 18.72 |
| AAPL | 2005 | 48.17 |
| AAPL | 2006 | 72.04 |
| AAPL | 2007 | 133.4 |
| AAPL | 2008 | 138.5 |
| AAPL | 2009 | 150.4 |
Joins
We now have experimental support for joins. This is a left-hash-join algorithm so the current algorithm is -
(tech.ml.dataset.functional/arggroup-by-int (lhs lhs-colname))- this returns a map of column-value->index-int32-array-list.- Run through
(rhs rhs-colname)finding values in the group-by hash map and building out left and right hand side final indexes. - Using appropriate final indexes, select columns from left and right hand sides.
Colname can be value in which case both datasets must contain that column or it
may be a tuple in which case it will be destructured like:
(let [[lhs-colname rhs-colname] colname] ...)
user> (def test-ds
(ds/->dataset "data/ames-house-prices/train.csv"
{:column-whitelist ["SalePrice" "1stFlrSF" "2ndFlrSF"]
:n-records 5}))
#'user/test-ds
user> test-ds
data/ames-house-prices/train.csv [4 3]:
| SalePrice | 1stFlrSF | 2ndFlrSF |
|-----------+----------+----------|
| 208500 | 856 | 854 |
| 181500 | 1262 | 0 |
| 223500 | 920 | 866 |
| 140000 | 961 | 756 |
user> (ds/inner-join "1stFlrSF"
(ds/set-dataset-name test-ds "left")
(ds/set-dataset-name test-ds "right"))
inner-join [4 5]:
| 1stFlrSF | SalePrice | 2ndFlrSF | right.SalePrice | right.2ndFlrSF |
|----------+-----------+----------+-----------------+----------------|
| 856 | 208500 | 854 | 208500 | 854 |
| 1262 | 181500 | 0 | 181500 | 0 |
| 920 | 223500 | 866 | 223500 | 866 |
| 961 | 140000 | 756 | 140000 | 756 |
(ds-join/right-join ["1stFlrSF" "2ndFlrSF"]
(ds/set-dataset-name test-ds "left")
(ds/set-dataset-name test-ds "right"))
right-outer-join [4 5]:
| 2ndFlrSF | SalePrice | 1stFlrSF | left.SalePrice | left.2ndFlrSF |
|----------+-----------+----------+----------------+---------------|
| 854 | 208500 | 856 | -2147483648 | -32768 |
| 0 | 181500 | 1262 | -2147483648 | -32768 |
| 866 | 223500 | 920 | -2147483648 | -32768 |
| 756 | 140000 | 961 | -2147483648 | -32768 |
user> (ds-join/left-join ["1stFlrSF" "2ndFlrSF"]
(ds/set-dataset-name test-ds "left")
(ds/set-dataset-name test-ds "right"))
left-outer-join [4 6]:
| 1stFlrSF | SalePrice | 2ndFlrSF | right.2ndFlrSF | right.SalePrice | right.1stFlrSF |
|----------+-----------+----------+----------------+-----------------+----------------|
| 961 | 140000 | 756 | -32768 | -2147483648 | -32768 |
| 920 | 223500 | 866 | -32768 | -2147483648 | -32768 |
| 856 | 208500 | 854 | -32768 | -2147483648 | -32768 |
| 1262 | 181500 | 0 | -32768 | -2147483648 | -32768 |
XLS, XLSX files
We use apache poi directly to generate datasets from xls and xlsx files. This feature is, like joins, very new.
user> (ds/head (ds/->dataset "test/data/file_example_XLS_1000.xls"))
Sheet1 [5 8]:
| 0 | First Name | Last Name | Gender | Country | Age | Date | Id |
|-------+------------+-----------+--------+---------------+-------+------------+------|
| 1.000 | Dulce | Abril | Female | United States | 32.00 | 15/10/2017 | 1562 |
| 2.000 | Mara | Hashimoto | Female | Great Britain | 25.00 | 16/08/2016 | 1582 |
| 3.000 | Philip | Gent | Male | France | 36.00 | 21/05/2015 | 2587 |
| 4.000 | Kathleen | Hanner | Female | United States | 25.00 | 15/10/2017 | 3549 |
| 5.000 | Nereida | Magwood | Female | United States | 58.00 | 16/08/2016 | 2468 |
In this example, we actually failed to parse the date because it is in an international format (day-month-year) and the dataset system automatically defaults to the American (month-day-year). In this case you actually have 2 options. You can reload the entire file and specify a datatype and DateTimeFormatter format for the column:
user> (ds/head (ds/->dataset "test/data/file_example_XLS_1000.xls" {:parser-fn
{"Date" [:local-date "dd/MM/yyyy"]}}))
Sheet1 [5 8]:
| 0 | First Name | Last Name | Gender | Country | Age | Date | Id |
|-------+------------+-----------+--------+---------------+-------+------------+------|
| 1.000 | Dulce | Abril | Female | United States | 32.00 | 2017-10-15 | 1562 |
| 2.000 | Mara | Hashimoto | Female | Great Britain | 25.00 | 2016-08-16 | 1582 |
| 3.000 | Philip | Gent | Male | France | 36.00 | 2015-05-21 | 2587 |
| 4.000 | Kathleen | Hanner | Female | United States | 25.00 | 2017-10-15 | 3549 |
| 5.000 | Nereida | Magwood | Female | United States | 58.00 | 2016-08-16 | 2468 |
Or you can reparse just that column using the above parse syntax:
user> (require '[tech.ml.dataset.column :as ds-col])
nil
user> (def unparsed (ds/->dataset "test/data/file_example_XLS_1000.xls"))
#'user/unparsed
user> (ds/head (ds/update-column unparsed "Date"
(partial ds-col/parse-column [:local-date "dd/MM/yyyy"])))
Sheet1 [5 8]:
| 0 | First Name | Last Name | Gender | Country | Age | Date | Id |
|-------+------------+-----------+--------+---------------+-------+------------+------|
| 1.000 | Dulce | Abril | Female | United States | 32.00 | 2017-10-15 | 1562 |
| 2.000 | Mara | Hashimoto | Female | Great Britain | 25.00 | 2016-08-16 | 1582 |
| 3.000 | Philip | Gent | Male | France | 36.00 | 2015-05-21 | 2587 |
| 4.000 | Kathleen | Hanner | Female | United States | 25.00 | 2017-10-15 | 3549 |
| 5.000 | Nereida | Magwood | Female | United States | 58.00 | 2016-08-16 | 2468 |
Writing A Dataset Out
These forms are supported for writing out a dataset:
(ds/write-csv! test-ds "test.csv")
(ds/write-csv! test-ds "test.tsv")
(ds/write-csv! test-ds "test.tsv.gz")
(ds/write-csv! test-ds out-stream)
If you want to use your own serialization system, then converting the dataset to a sequence of maps presents a slow but effective way forward:
- pure csv
(tech.io/mapseq->csv! "file://test.csv" (dataset/mapseq-reader test-ds ))
- tsv
(io/mapseq->csv! "file://test.tsv"
(dataset/mapseq-reader test-ds )
:separator \tab)
- gzipped tsv
user> (with-open [outs (io/gzip-output-stream! "file://test.tsv.gz")]
(io/mapseq->csv!
(dataset/mapseq-reader test-ds )
:separator \tab))
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