Expectation-Maximisation (EM) for learning with latent variable (#175)

* Add Expectation Maximisation (EM) algorithm implementation

* Massively scale up E-step by converting multiindexed pandas dataframe into dict of dict

* Add a tutorial on latent variable model based on EM algorithm

* Add test cases for the EM algorithm and ensure 100% test coverage

* Update sklearn tutorials

* Add note on EM algorithm into RELEASE.md

* Reorganise .dot and .jpg files under docs/source/03_tutorial/supporting_files folder

* Fix link in plotting tutorial

* Exclude docs/source/03_tutorial from end-of-file check

* Update doc_requirements.txt

* Add DOT to JPG conversion steps into build-docs.sh

.pre-commit-config.yaml

@@ -11,6 +11,7 @@ repos:
         stages: [commit, manual]
     -   id: end-of-file-fixer
         stages: [commit, manual]
+        exclude: ^docs/source/03_tutorial
     -   id: check-yaml  # Checks yaml files for parseable syntax.
 #        exclude:
     -   id: check-json  # Checks json files for parseable syntax.

RELEASE.md

@@ -1,12 +1,13 @@
 # Upcoming release
 
-# Release 0.10.1
+# Release 0.11.0
+* Add expectation-maximisation (EM) algorithm to learn with latent variables
+* Fix infinite loop at `.query()` after a `.do_intervention()` that splits
+  the graph into two or more subgraphs, as per #100, #45
 * Fix decision tree and mdlp discretisations bug when input data is shuffled
 * Fix broken URLs in FAQ documentation, as per #113 and #125
 * Add a link to `PyGraphviz` installation guide under the installation prerequisites
 * Fix integer index type checking for timeseries data, as per #74 and #86
-* Fix infinite loop at `.query()` after a `.do_intervention()` that splits
-  the graph into two or more subgraphs, as per #100, #45
 * Add GPU support to Pytorch implementation, as requested in #56 and #114
 
 # Release 0.10.0

causalnex/estimator/__init__.py

@@ -0,0 +1,35 @@
+# Copyright 2019-2020 QuantumBlack Visual Analytics Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
+# NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS
+# BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN
+# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+# The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo
+# (either separately or in combination, "QuantumBlack Trademarks") are
+# trademarks of QuantumBlack. The License does not grant you any right or
+# license to the QuantumBlack Trademarks. You may not use the QuantumBlack
+# Trademarks or any confusingly similar mark as a trademark for your product,
+#     or use the QuantumBlack Trademarks in any other manner that might cause
+# confusion in the marketplace, including but not limited to in advertising,
+# on websites, or on software.
+#
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+``causalnex.network`` provides functionality to learn joint probability distribution of networks.
+"""
+
+__all__ = ["EMSingleLatentVariable"]
+
+from .em import EMSingleLatentVariable