🚀 Fix docs and decsr (#70)

maks-sh · web-flow · commit 161c1b74bd97 · 2021-02-07T03:17:16.000+03:00
diff --git a/docs/api/datasets/fetch_criteo.rst b/docs/api/datasets/fetch_criteo.rst
@@ -1,3 +1,5 @@
+.. _Criteo:
+
 **************************************
 `sklift.datasets <./>`_.fetch_criteo
 **************************************
diff --git a/docs/api/datasets/fetch_hillstrom.rst b/docs/api/datasets/fetch_hillstrom.rst
@@ -1,7 +1,9 @@
+.. _Hillstrom:
+
 ****************************************
 `sklift.datasets <./>`_.fetch_hillstrom
 ****************************************
 
 .. autofunction:: sklift.datasets.datasets.fetch_hillstrom
 
-.. include:: ../../../sklift/datasets/descr/lenta.rst
+.. include:: ../../../sklift/datasets/descr/hillstrom.rst
diff --git a/docs/api/datasets/fetch_lenta.rst b/docs/api/datasets/fetch_lenta.rst
@@ -1,3 +1,5 @@
+.. _Lenta:
+
 ***********************************
 `sklift.datasets <./>`_.fetch_lenta
 ***********************************
diff --git a/docs/api/datasets/fetch_x5.rst b/docs/api/datasets/fetch_x5.rst
@@ -1,3 +1,5 @@
+.. _X5:
+
 ***********************************
 `sklift.datasets <./>`_.fetch_x5
 ***********************************
diff --git a/requirements.txt b/requirements.txt
@@ -1,4 +1,5 @@
 scikit-learn>=0.21.0
 numpy>=1.16
 pandas
-matplotlib
+matplotlib
+requests
diff --git a/sklift/datasets/datasets.py b/sklift/datasets/datasets.py
diff --git a/sklift/datasets/descr/hillstrom.rst b/sklift/datasets/descr/hillstrom.rst
@@ -4,17 +4,20 @@ Kevin Hillstrom Dataset: MineThatData
 Data description
 ################
 
-This is a copy of `MineThatData E-Mail Analytics And Data Mining Challenge dataset <https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html/>`_.
+This is a copy of `MineThatData E-Mail Analytics And Data Mining Challenge dataset <https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html>`_.
 
-date: March 20, 2008
-
-This dataset contains 64,000 customers who last purchased within twelve months. The customers were involved in an e-mail test.
+This dataset contains 64,000 customers who last purchased within twelve months.
+The customers were involved in an e-mail test.
 
 * 1/3 were randomly chosen to receive an e-mail campaign featuring Mens merchandise.
 * 1/3 were randomly chosen to receive an e-mail campaign featuring Womens merchandise.
 * 1/3 were randomly chosen to not receive an e-mail campaign.
 
-During a period of two weeks following the e-mail campaign, results were tracked. Your job is to tell the world if the Mens or Womens e-mail campaign was successful.
+During a period of two weeks following the e-mail campaign, results were tracked.
+Your job is to tell the world if the Mens or Womens e-mail campaign was successful.
+
+Fields
+################
 
 Historical customer attributes at your disposal include:
 
@@ -30,9 +33,10 @@ Historical customer attributes at your disposal include:
 Another variable describes the e-mail campaign the customer received:
 
 * Segment
-* Mens E-Mail
-* Womens E-Mail
-* No E-Mail
+
+    * Mens E-Mail
+    * Womens E-Mail
+    * No E-Mail
 
 Finally, we have a series of variables describing activity in the two weeks following delivery of the e-mail campaign:
 
diff --git a/sklift/datasets/descr/lenta.rst b/sklift/datasets/descr/lenta.rst
@@ -8,16 +8,6 @@ An uplift modeling dataset containing data about Lenta's customers grociery shop
 
 Source: **BigTarget Hackathon** hosted by Lenta and Microsoft in summer 2020.
 
-
-Key figures
-################
-* Format: CSV
-* Size: 153M (compressed) 567M (uncompressed)
-* Rows: 687 029
-* Response Ratio: 0.1
-* Treatment Ratio: 0.75
-
-
 Fields
 ################
 
@@ -115,4 +105,12 @@ Major features:
     * - stdev_discount_depth_[15d,1m]
       - discount sum coefficient of variation for 15 days, 1 month
 
+Key figures
+################
+
+* Format: CSV
+* Size: 153M (compressed) 567M (uncompressed)
+* Rows: 687 029
+* Response Ratio: 0.1
+* Treatment Ratio: 0.75
 
diff --git a/sklift/datasets/descr/x5.rst b/sklift/datasets/descr/x5.rst
@@ -3,9 +3,10 @@ X5 RetailHero Uplift Modeling Dataset
 
 The dataset is provided by X5 Retail Group at the RetailHero hackaton hosted in winter 2019.
 
-The dataset contains raw retail customer purchaces, raw information about products and general info about customers.
+The dataset contains raw retail customer purchases, raw information about products and general info about customers.
 
-`Hackaton website <https://ods.ai/competitions/x5-retailhero-uplift-modeling/data/>`_.
+
+`Machine learning competition website <https://ods.ai/competitions/x5-retailhero-uplift-modeling/data/>`_.
 
 Data description
 ################
@@ -14,7 +15,6 @@ Data contains several parts:
 
 * train.csv: a subset of clients for training. The column *treatment_flg* indicates if there was a communication. The column *target* shows if there was a purchase afterward;
 * clients.csv: general info about clients;
-* products.csv: general info about stock items;
 * purchases.csv: clients’ purchase history prior to communication.
 
 Fields

Original file line number	Diff line number	Diff line change
`@@ -1,3 +1,5 @@`
	`1`	`+.. _Criteo:`
	`2`	`+`
`1`	`3`	`**************************************`
`2`	`4`	`sklift.datasets <./>`_.fetch_criteo
`3`	`5`	`**************************************`
Original file line number	Diff line number	Diff line change
`@@ -1,3 +1,5 @@`
	`1`	`+.. _Lenta:`
	`2`	`+`
`1`	`3`	`***********************************`
`2`	`4`	`sklift.datasets <./>`_.fetch_lenta
`3`	`5`	`***********************************`
Original file line number	Diff line number	Diff line change
`@@ -1,3 +1,5 @@`
	`1`	`+.. _X5:`
	`2`	`+`
`1`	`3`	`***********************************`
`2`	`4`	`sklift.datasets <./>`_.fetch_x5
`3`	`5`	`***********************************`