Merge pull request #2968 from RaRe-Technologies/migrate_tutorials

[MRG] Migrate tutorials & how-tos to 4.0.0

docs/src/auto_examples/core/run_core_concepts.ipynb

@@ -249,7 +249,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimg = mpimg.imread('run_core_concepts.png')\nimgplot = plt.imshow(img)\nplt.axis('off')\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimg = mpimg.imread('run_core_concepts.png')\nimgplot = plt.imshow(img)\n_ = plt.axis('off')"
       ]
     }
   ],

docs/src/auto_examples/core/run_core_concepts.py

@@ -327,5 +327,4 @@
 import matplotlib.image as mpimg
 img = mpimg.imread('run_core_concepts.png')
 imgplot = plt.imshow(img)
-plt.axis('off')
-plt.show()
+_ = plt.axis('off')

docs/src/auto_examples/core/run_core_concepts.py.md5

@@ -1 +1 @@
-d835332ead51b01a01d6b2483f8bf180
+e562837df1242b45d0ab623f5a5254f0

docs/src/auto_examples/core/run_core_concepts.rst

@@ -1,10 +1,12 @@
-.. note::
-    :class: sphx-glr-download-link-note
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
 
-    Click :ref:`here <sphx_glr_download_auto_examples_core_run_core_concepts.py>` to download the full example code
-.. rst-class:: sphx-glr-example-title
+        Click :ref:`here <sphx_glr_download_auto_examples_core_run_core_concepts.py>`     to download the full example code
+    .. rst-class:: sphx-glr-example-title
 
-.. _sphx_glr_auto_examples_core_run_core_concepts.py:
+    .. _sphx_glr_auto_examples_core_run_core_concepts.py:
 
 
 Core Concepts
@@ -24,6 +26,7 @@ This tutorial introduces Documents, Corpora, Vectors and Models: the basic conce
 
 
 
+
 The core concepts of ``gensim`` are:
 
 1. :ref:`core_concepts_document`: some text.
@@ -54,6 +57,7 @@ paragraph (i.e., journal article abstract), a news article, or a book.
 
 
 
+
 .. _core_concepts_corpus:
 
 Corpus
@@ -102,6 +106,7 @@ It consists of 9 documents, where each document is a string consisting of a sing
 
 
 
+
 .. Important::
   The above example loads the entire corpus into memory.
   In practice, corpora may be very large, so loading them into memory may be impossible.
@@ -169,6 +174,7 @@ a delimiter).
 
 
 
+
 Before proceeding, we want to associate each word in the corpus with a unique
 integer ID. We can do this using the :py:class:`gensim.corpora.Dictionary`
 class.  This dictionary defines the vocabulary of all words that our
@@ -197,6 +203,7 @@ processing knows about.
 
 
 
+
 Because our corpus is small, there are only 12 different tokens in this
 :py:class:`gensim.corpora.Dictionary`. For larger corpuses, dictionaries that
 contains hundreds of thousands of tokens are quite common.
@@ -287,6 +294,7 @@ into these 12-dimensional vectors. We can see what these IDs correspond to:
 
 
 
+
 For example, suppose we wanted to vectorize the phrase "Human computer
 interaction" (note that this phrase was not in our original corpus). We can
 create the bag-of-word representation for a document using the ``doc2bow``
@@ -316,6 +324,7 @@ counts:
 
 
 
+
 The first entry in each tuple corresponds to the ID of the token in the
 dictionary, the second corresponds to the count of this token.
 
@@ -357,6 +366,7 @@ We can convert our entire original corpus to a list of vectors:
 
 
 
+
 Note that while this list lives entirely in memory, in most applications you
 will want a more scalable solution. Luckily, ``gensim`` allows you to use any
 iterator that returns a single document vector at a time. See the
@@ -427,6 +437,7 @@ our corpus and transforming the string "system minors":
 
 
 
+
 The ``tfidf`` model again returns a list of tuples, where the first entry is
 the token ID and the second entry is the tf-idf weighting. Note that the ID
 corresponding to "system" (which occurred 4 times in the original corpus) has
@@ -457,6 +468,7 @@ preparation for similarity queries:
 
 
 
+
 and to query the similarity of our query document ``query_document`` against every document in the corpus:
 
 
@@ -481,6 +493,7 @@ and to query the similarity of our query document ``query_document`` against eve
 
 
 
+
 How to read this output?
 Document 3 has a similarity score of 0.718=72%, document 2 has a similarity score of 42% etc.
 We can make this slightly more readable by sorting:
@@ -514,6 +527,7 @@ We can make this slightly more readable by sorting:
 
 
 
+
 Summary
 -------
 
@@ -543,32 +557,24 @@ There's still much more to learn about :ref:`sphx_glr_auto_examples_core_run_cor
     import matplotlib.image as mpimg
     img = mpimg.imread('run_core_concepts.png')
     imgplot = plt.imshow(img)
-    plt.axis('off')
-    plt.show()
+    _ = plt.axis('off')
 
 
 
 .. image:: /auto_examples/core/images/sphx_glr_run_core_concepts_001.png
+    :alt: run core concepts
     :class: sphx-glr-single-img
 
 
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    /Volumes/work/workspace/gensim_misha/docs/src/gallery/core/run_core_concepts.py:331: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      plt.show()
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  1.265 seconds)
+   **Total running time of the script:** ( 0 minutes  1.675 seconds)
 
-**Estimated memory usage:**  36 MB
+**Estimated memory usage:**  37 MB
 
 
 .. _sphx_glr_download_auto_examples_core_run_core_concepts.py:
@@ -581,13 +587,13 @@ There's still much more to learn about :ref:`sphx_glr_auto_examples_core_run_cor
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-python
 
      :download:`Download Python source code: run_core_concepts.py <run_core_concepts.py>`
 
 
 
-  .. container:: sphx-glr-download
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
 
      :download:`Download Jupyter notebook: run_core_concepts.ipynb <run_core_concepts.ipynb>`
 

docs/src/auto_examples/core/run_corpora_and_vector_spaces.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nCorpora and Vector Spaces\n=========================\n\nDemonstrates transforming text into a vector space representation.\n\nAlso introduces corpus streaming and persistence to disk in various formats.\n"
+        "\nCorpora and Vector Spaces\n=========================\n\nDemonstrates transforming text into a vector space representation.\n\nAlso introduces corpus streaming and persistence to disk in various formats.\n\n"
       ]
     },
     {
@@ -396,13 +396,6 @@
         "What Next\n---------\n\nRead about `sphx_glr_auto_examples_core_run_topics_and_transformations.py`.\n\nReferences\n----------\n\nFor a complete reference (Want to prune the dictionary to a smaller size?\nOptimize converting between corpora and NumPy/SciPy arrays?), see the `apiref`.\n\n.. [1] This is the same corpus as used in\n       `Deerwester et al. (1990): Indexing by Latent Semantic Analysis <http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf>`_, Table 2.\n\n"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Here we show a pretty fastText logo so that our gallery picks it up as a thumbnail.\n\n\n"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -411,7 +404,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimg = mpimg.imread('run_corpora_and_vector_spaces.png')\nimgplot = plt.imshow(img)\nplt.axis('off')\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimg = mpimg.imread('run_corpora_and_vector_spaces.png')\nimgplot = plt.imshow(img)\n_ = plt.axis('off')"
       ]
     }
   ],
@@ -431,7 +424,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.1"
+      "version": "3.6.5"
     }
   },
   "nbformat": 4,

docs/src/auto_examples/core/run_corpora_and_vector_spaces.py

@@ -303,12 +303,8 @@ def __iter__(self):
 # .. [1] This is the same corpus as used in
 #        `Deerwester et al. (1990): Indexing by Latent Semantic Analysis <http://www.cs.bham.ac.uk/~pxt/IDA/lsa_ind.pdf>`_, Table 2.
 
-###############################################################################
-# Here we show a pretty fastText logo so that our gallery picks it up as a thumbnail.
-#
 import matplotlib.pyplot as plt
 import matplotlib.image as mpimg
 img = mpimg.imread('run_corpora_and_vector_spaces.png')
 imgplot = plt.imshow(img)
-plt.axis('off')
-plt.show()
+_ = plt.axis('off')

docs/src/auto_examples/core/run_corpora_and_vector_spaces.py.md5

@@ -1 +1 @@
-62ddd3c9d328a2b81ecdbb4f0fb203b2
+e017de81683bfd2f6005a3186bfc1eb3