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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Feature engineering
This notebook will teach you how to extract feature values using revscoring's built-in feature library as well as to build your own features.  
Set up the feature extractor
This line constructs a "feature extractor" that uses Wikipedia's API.  We'll need to use it later, so we'll construct it first.
Step1:  Extract features
The following line demonstrates a simple feature extraction.  We'll extract two features
Step2:  Defining a custom feature
The next block defines a new feature and sets the dependencies to be the two features we just extracted.  This feature represents the proportion of characters in the current version of the page that the current edit is responsible for adding.
Step3:  There's easier ways that we can do this though.  revscoring.Feature overloads simple mathematical operators to allow you to do math with features and get a feature returned.  revscoring.features.modifiers contains a set of basic functions that do the same.  This code roughly corresponds to what's going on above.
Step4:  While the implicit pattern is quicker and easier than the explicit pattern, it's name can not be customized.
Step5:  Extracting datasources
There's a also a set of revscoring.Datasource's that are part of the dependency injection system.  These "datasources" represent the data needed for feature generation.  We can extract them just like revscoring.Feature's.
Step6:  OK.  Let's define a new feature for counting the number of templates added.  I'll make use of mwparserfromhell to do this.  See the docs.
Step7:  Debugging
There's some facilities in place to help you make sense of issues when they arise.  The most important is the draw function.
Step8:  In the tree structure above, you can see how our new feature depends on wikitext.revision.diff.segments_added which depends on wikitext.revision.diff.operations which depends (as you might imagine) on the current and parent revision.  Some features can get quite complicated.
Step9:  The dependency injection system will only solve a unique dependency once for a given tree.  So, even though &lt;revision.parent.text&gt; appears twice above, it will only be extracted once and then cached.  This allows for multiple features to share large sections of their dependency trees -- and therefor minimize resource usage.
Errors during extraction
A revscoring.Extractor should be expected to throw an exception if it cannot find a missing resource during extraction.  These messages are intented to clearly convey what went wrong.
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Python Code:
from revscoring.extractors import api
import mwapi
extractor = api.Extractor(mwapi.Session("https://en.wikipedia.org",
                                        user_agent="Revscoring feature demo ahalfaker@wikimedia.org"))
Explanation: Feature engineering
This notebook will teach you how to extract feature values using revscoring's built-in feature library as well as to build your own features.  
Set up the feature extractor
This line constructs a "feature extractor" that uses Wikipedia's API.  We'll need to use it later, so we'll construct it first.
End of explanation
from revscoring.features import wikitext
list(extractor.extract(123456789, [wikitext.revision.chars, 
                                   wikitext.revision.diff.chars_added]))
Explanation: Extract features
The following line demonstrates a simple feature extraction.  We'll extract two features: wikitext.revision.chars, the number of characters added; and wikitext.revision.diff.chars_added, the number of characters in the entire revision.  Note that we wrap the call in a list() because it returns a generator.
End of explanation
from revscoring import Feature
chars_added_ratio_explicit = Feature(
    "chars_added_ratio_explicit", 
    lambda a,c: a/max(c, 1), # Prevents divide by zero
    depends_on=[wikitext.revision.diff.chars_added, 
                wikitext.revision.chars],
    returns=float)
list(extractor.extract(123456789, [chars_added_ratio_explicit]))
Explanation: Defining a custom feature
The next block defines a new feature and sets the dependencies to be the two features we just extracted.  This feature represents the proportion of characters in the current version of the page that the current edit is responsible for adding.
End of explanation
from revscoring.features import modifiers
chars_added_ratio_implicit = (wikitext.revision.diff.chars_added /
                              modifiers.max(wikitext.revision.chars, 1))
list(extractor.extract(123456789, [chars_added_ratio_implicit]))
Explanation: There's easier ways that we can do this though.  revscoring.Feature overloads simple mathematical operators to allow you to do math with features and get a feature returned.  revscoring.features.modifiers contains a set of basic functions that do the same.  This code roughly corresponds to what's going on above.
End of explanation
chars_added_ratio_explicit, chars_added_ratio_implicit
Explanation: While the implicit pattern is quicker and easier than the explicit pattern, it's name can not be customized.
End of explanation
list(extractor.extract(662953550, [wikitext.revision.diff.datasources.segments_added,
                                   wikitext.revision.diff.datasources.segments_removed]))
Explanation: Extracting datasources
There's a also a set of revscoring.Datasource's that are part of the dependency injection system.  These "datasources" represent the data needed for feature generation.  We can extract them just like revscoring.Feature's.
End of explanation
import mwparserfromhell as mwp
templates_added = Feature("templates_added", 
                          lambda add_segments: sum(len(mwp.parse(s).filter_templates()) > 0 for s in add_segments),
                          depends_on=[wikitext.revision.diff.datasources.segments_added],
                          returns=int)
list(extractor.extract(662953550, [templates_added]))
Explanation: OK.  Let's define a new feature for counting the number of templates added.  I'll make use of mwparserfromhell to do this.  See the docs.
End of explanation
from revscoring.dependencies import draw
print(draw(templates_added))
Explanation: Debugging
There's some facilities in place to help you make sense of issues when they arise.  The most important is the draw function.
End of explanation
print(draw(wikitext.revision.diff.number_prop_delta_sum))
Explanation: In the tree structure above, you can see how our new feature depends on wikitext.revision.diff.segments_added which depends on wikitext.revision.diff.operations which depends (as you might imagine) on the current and parent revision.  Some features can get quite complicated.
End of explanation
try:
    list(extractor.extract(2, [wikitext.revision.diff.words_added]))
except Exception as e:
    print(e)
try:
    list(extractor.extract(262721924, [wikitext.revision.diff.words_added]))
except Exception as e:
    print(e)
from revscoring.features import revision_oriented
try:
    list(extractor.extract(172665816, [revision_oriented.revision.comment_matches("foo")]))
except Exception as e:
    print(e)
from revscoring.features import temporal
try:
    list(extractor.extract(591839757, [revision_oriented.revision.user.text_matches("foo")]))
except Exception as e:
    print(e)
Explanation: The dependency injection system will only solve a unique dependency once for a given tree.  So, even though &lt;revision.parent.text&gt; appears twice above, it will only be extracted once and then cached.  This allows for multiple features to share large sections of their dependency trees -- and therefor minimize resource usage.
Errors during extraction
A revscoring.Extractor should be expected to throw an exception if it cannot find a missing resource during extraction.  These messages are intented to clearly convey what went wrong.
End of explanation
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