It annoys me to the extreme that sklearn [forces warnings][1].

I started using this at the top of main.py:

    def warn(*args, **kwargs):
        pass
    import warnings
    warnings.warn = warn
    
    #... import sklearn stuff...


  [1]: https://github.com/scikit-learn/scikit-learn/issues/2531

They have [changed their warning policy in 2013][1]. You can ignore warnings (also specific types) with something like this:

    import warnings
    warnings.filterwarnings(&quot;ignore&quot;, category=DeprecationWarning)


  [1]: https://github.com/scikit-learn/scikit-learn/pull/2619


//EDIT: in the comments below, Reed Richards points out that `the filterwarnings call needs to be in the file that calls the function that gives the warning.`
I hope this helps those who experienced problems with this solution.

How would I go about animating from one color to another in React Native. I&#39;ve found that by interpolating an Animated.Value you can animate colors by:

&lt;!-- language-all: lang-js --&gt;

    var BLACK = 0;
    var RED = 1;
    var BLUE = 2;
    
    backgroundColor: this.state.color.interpolate({
      inputRange: [BLACK, RED, BLUE],
      outputRange: [&#39;rgb(0, 0, 0)&#39;, &#39;rgb(255, 0, 0)&#39;, &#39;rgb(0, 0, 255)&#39;]
    })

and

    Animated.timing(this.state.color, {toValue: RED}).start();

But using this method, going from BLACK to BLUE, you have to go through red. Add more colors to the mix and you end up in a 1980s disco.

Is there another way of doing this that allows you to go straight from one color to another?  

Animating backgroundColor in React Native

In Xcode 7 GM I started to get this warning:

&gt; Pointer is missing a nullability type specifier (_Nonnull, _Nullable, or _Null_unspecified)

In the following function declaration (NSUserDefaults extension)

    - (void)setObject:(nullable id)value
               forKey:(NSString *)defaultName
        objectChanged:(void(^)(NSUserDefaults *userDefaults, id value))changeHandler
        objectRamains:(void(^)(NSUserDefaults *userDefaults, id value))remainHandler;

[![Screenshot][1]][1]

Why this warning is showing and how should I fix it?


  [1]: https://i.stack.imgur.com/c4TE2.png

Pointer is missing a nullability type specifier

I would like to ignore warnings from all packages when I am teaching, but scikit-learn seems to work around the use of the `warnings` package to control this. For example:


    with warnings.catch_warnings():
        warnings.simplefilter(&quot;ignore&quot;)
        from sklearn import preprocessing

    /usr/local/lib/python3.5/site-packages/sklearn/utils/fixes.py:66: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
      if &#39;order&#39; in inspect.getargspec(np.copy)[0]:
    /usr/local/lib/python3.5/site-packages/sklearn/utils/fixes.py:358: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
      if &#39;exist_ok&#39; in inspect.getargspec(os.makedirs).args:

Am I using this module incorrectly, or is sklearn doing something its not supposed to?

Eliminating warnings from scikit-learn

I would like to ignore warnings from all packages when I am teaching, but scikit-learn seems to work around the use of the <code>warnings</code> package to control this. For example:
<pre><code class="hljs language-css">with warnings.catch_warnings():
 warnings.simplefilter("ignore")
 from sklearn import preprocessing

/usr/local/lib/python3.5/site-packages/sklearn/utils/fixes.py:66: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
 if 'order' in inspect.getargspec(np.copy)[0]:
/usr/local/lib/python3.5/site-packages/sklearn/utils/fixes.py:358: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead
 if 'exist_ok' in inspect.getargspec(os.makedirs).args:
</code></pre>
Am I using this module incorrectly, or is sklearn doing something its not supposed to?

I have installed pip and ez setup. I also checked the system path and I can see the module in the folder structure. Still when i try to run pip command, I get an Import error saying no module named pip. I am running 32bit python on a windows7 machine

Why am I getting ImportError: No module named pip &#39; right after installing pip?

I&#39;m trying to zip a directory called `test_dicoms` to a zip file named `test_dicoms.zip` using the following code:

```py
shutil.make_archive(&#39;/home/code/test_dicoms&#39;, &#39;zip&#39;, &#39;/home/code/test_dicoms&#39;)
```

The problem is that when I unzip it, all of the files that were in `/test_dicoms/` are extracted to `/home/code/` instead of the folder `/test_dicoms/` and all of it&#39;s contained files being extracted to `/home/code/`. So `/test_dicoms/` has a file called `foo.txt` and after I zip and unzip `foo.txt`&#39;s path is `/home/code/foo.txt` as opposed to `/home/code/test_dicoms/foo.txt`. How do I fix this? Also, some of the directories I&#39;m working with are very large. Will I need to add anything to my code to make it ZIP64 or is the function smart enough to do that automatically?

Here&#39;s what&#39;s currently in the archive created:

&lt;!-- language: lang-none --&gt;

    [gwarner@jazz gwarner]$ unzip -l test_dicoms.zip
    Archive: test_dicoms.zip
    Length    Date       Time  Name
    --------- ---------- ----- ----
        93324 09-17-2015 16:05 AAscout_b_000070
        93332 09-17-2015 16:05 AAscout_b_000125
        93332 09-17-2015 16:05 AAscout_b_000248


Compressing directory using shutil.make_archive() while preserving directory structure

Say I have two networkx graphs, `G` and `H`:

    G=nx.Graph()
    fromnodes=[0,1,1,1,1,1,2]
    tonodes=[1,2,3,4,5,6,7]
    for x,y in zip(fromnodes,tonodes):
        G.add_edge(x,y)

    H=nx.Graph()
    fromnodes=range(2,8)
    tonodes=range(8,14)
    for x,y in zip(fromnodes,tonodes):
        H.add_edge(x,y)

**What is the best way to join the two networkx graphs?**  

I&#39;d like to preserve the node names (note the common nodes, 2 to 7). When I used `nx.disjoint_union(G,H)`, this did not happen:

    &gt;&gt;&gt; G.nodes()
    [0, 1, 2, 3, 4, 5, 6, 7]
    &gt;&gt;&gt; H.nodes()
    [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
    &gt;&gt;&gt; Un= nx.disjoint_union(G,H)
    &gt;&gt;&gt; Un.nodes()
    [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
    # 
The `H` node labels were changed (not what I want). I want to join the graphs at the nodes with the same number.

*Note. This is not a duplicate of https://stackoverflow.com/questions/21806751/combine-two-weighted-graphs-in-networkx.* 

Combine (join) networkx Graphs

I am trying to import the izip module like so: 

    from itertools import izip

However after recently changing over from Python 2.7 to 3 - it doesn&#39;t seem to work.

I am trying to write to a csv file:

    writer.writerows(izip(variable1,2))

But I have no luck. Still encounter an error.

importing izip from itertools module gives NameError in Python 3.x

This is a very basic question, I just can not seem to find an answer.

I have a dataframe like this, called df:

      A     B     C
     a.1   b.1   c.1
     a.2   b.2   c.2
     a.3   b.3   c.3

Then I extract all the rows from df, where column &#39;B&#39; has a value of &#39;b.2&#39;.  I assign these results to df_2.  

    df_2 = df[df[&#39;B&#39;] == &#39;b.2&#39;]

df_2 becomes:

      A     B     C
     a.2   b.2   c.2

Then, I copy all the values in column &#39;B&#39; to a new column named &#39;D&#39;. Causing df_2 to become: 

      A     B     C     D
     a.2   b.2   c.2   b.2

When I preform an assignment like this:

    df_2[&#39;D&#39;] = df_2[&#39;B&#39;]

I get the following warning:

&gt; A value is trying to be set on a copy of a slice from a DataFrame. Try
&gt; using .loc[row_indexer,col_indexer] = value instead
&gt; 
&gt; See the the caveats in the documentation:
&gt; http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

-----

I have also tried using .loc when creating df_2 like this:

    df_2 = df.loc[df[&#39;B&#39;] == &#39;b.2&#39;]

However, I still get the warning. 

Any help is greatly appreciated.

Copy all values in a column to a new column in a pandas dataframe

I can&#39;t figure out how the `sklearn.pipeline.Pipeline` works exactly.

There are a few explanation in the [doc](http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). For example what do they mean by:

&gt; Pipeline of transforms with a final estimator.

To make my question clearer, what are `steps`? How do they work?

**Edit**

Thanks to the answers I can make my question clearer:

When I call pipeline and pass, as steps, two transformers and one estimator, e.g:

    pipln = Pipeline([(&quot;trsfm1&quot;,transformer_1),
                      (&quot;trsfm2&quot;,transformer_2),
                      (&quot;estmtr&quot;,estimator)])

What happens when I call this?

    pipln.fit()
    OR
    pipln.fit_transform()
   
I can&#39;t figure out how an estimator can be a transformer and how a transformer can be fitted.

What is exactly sklearn.pipeline.Pipeline?

I have a binary prediction model trained by logistic regression algorithm. I want know which features(predictors) are more important for the decision of positive or negative class. I know there is `coef_` parameter comes from the scikit-learn package, but I don&#39;t know whether it is enough to for the importance. Another thing is how I can evaluate the `coef_` values in terms of the importance for negative and positive classes. I also read about standardized regression coefficients and I don&#39;t know what it is.

Lets say there are features like size of tumor, weight of tumor, and etc to make a decision for a test case like malignant or not malignant. I want to know which of the features are more important for malignant and not malignant prediction. Does it make sort of sense?


How to find the importance of the features for a logistic regression model?

I need to fit `RandomForestRegressor` from `sklearn.ensemble`.

    forest = ensemble.RandomForestRegressor(**RF_tuned_parameters)
    model = forest.fit(train_fold, train_y)
    yhat = model.predict(test_fold)

This code always worked until I made some preprocessing of data (`train_y`).
The error message says:

&gt;    DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
&gt;
&gt;    model = forest.fit(train_fold, train_y)

Previously `train_y` was a Series, now it&#39;s numpy array (it is a column-vector). If I apply `train_y.ravel()`, then it becomes a row vector and no error message appears, through the prediction step takes very long time (actually it never finishes...).

In the docs of `RandomForestRegressor` I found that `train_y` should be defined as `y : array-like, shape = [n_samples] or [n_samples, n_outputs]`
Any idea how to solve this issue?




A column-vector y was passed when a 1d array was expected

Is there any way to have a progress bar to the fit method in scikit-learn ? 

Is it possible to include a custom one with something like [Pyprind][1] ?


  [1]: https://github.com/rasbt/pyprind

A progress bar for scikit-learn?

I&#39;ve read from the [relevant documentation](https://scikit-learn.org/stable/modules/tree.html#tips-on-practical-use) that :  

&gt;Class balancing can be done by sampling an equal number of samples from each class, or preferably by normalizing the sum of the sample weights (`sample_weight`) for each class to the same value.

But, it is still unclear to me how this works.  If I set `sample_weight` with an array of only two possible values, `1`&#39;s and `2`&#39;s, does this mean that the samples with `2`&#39;s will get sampled twice as often as the samples with `1`&#39;s when doing the bagging?  I cannot think of a practical example for this.  





What does `sample_weight` do to the way a `DecisionTreeClassifier` works in sklearn?

 
I receive this lint warning when analysing my code (Analyse &gt; Inspect Codes) on Android studios.

&gt; App is not indexable by Google Search;  consider adding at least one Activity with an ACTION-VIEW intent-filler. See issue explanation for more details.

    
What is this warning, and how do I make my app indexable by Google Search?
it sounds important for SEO, but I can&#39;t find any details on Google.
 
I also like to know how to access the &quot;Issue Explanation&quot; from android studio.

[![enter image description here][1]][1]


  [1]: http://i.stack.imgur.com/aZ48Q.png


**Edit:**

&quot;App is not indexable by Google Search&quot; was the old warning. The new warning is 
&quot;Missing support for Firebase App Indexing&quot;

Missing support for Firebase App Indexing (android lint)

In my Kotlin Android project, I am using a function that has been deprecated starting from api 23, which is quite recent. So I need a way to disable those deprecated warnings. Is there an easy way to do so?


kotlin suppress warning deprecated for Android

In git if you checkout a commit directly you get a big fat warning starting with:

    &quot;You are in &#39;detached HEAD&#39; state. You can look around ...&quot;

It&#39;s fine - I intend to be in detached HEAD state. However I am using this in a script and I don&#39;t want this warning in the output logs but I do want the normal output.

My &quot;ugly&quot; workaround now is to run the same command twice, first with -q to hide the warning, and one more time to get the normal output: `HEAD is now at deadbeef... Message` since the warning is only printed once.

Can the warning be disabled so I can avoid workarounds or parsing the output ?

Disable warning about detached HEAD

Can you explain me why react show warning `Warning: validateDOMNesting(...): #text cannot appear as a child of &lt;tr&gt;. See Router &gt; RouterContext &gt; CarWashPage &gt; AllCarWashTable &gt; tr &gt; #text.`? I don&#39;t see any text inside tag `tr`

**Code that renders table**

    export default class AllCarWashTable extends React.Component{

    constructor(props) {
        super(props);
        this.generateHeaders = this.generateHeaders.bind(this);
        this.generateRows = this.generateRows.bind(this);
    };

    static propTypes = {
        cols : React.PropTypes.array.isRequired,
        rows : React.PropTypes.array.isRequired
    }

    generateHeaders() {
        let cols = this.props.cols;  // [{key, label}]
        return cols.map(function(colData) {
            return &lt;th key={colData.key}&gt; {colData.label} &lt;/th&gt;;
        });
    }

    generateRows() {
        let cols = this.props.cols,  // [{key, label}]
            data = this.props.rows;
        if (this.props.rows.length &gt; 0) {
            return data.map(function(item) {
                var cells = cols.map(function(colData) {
                    return &lt;td key={colData.key}&gt; {item[colData.key]} &lt;/td&gt;;
                });
                return &lt;tr key={item.id}&gt; {cells} &lt;/tr&gt;;
            });
        }
    }

    render(){
        let headers = this.generateHeaders();
        let rows = this.generateRows();

        return (
             &lt;table className=&quot;table table-hove&quot;&gt;
                 &lt;thead&gt;
                    &lt;tr&gt;
                        {headers}
                    &lt;/tr&gt;
                 &lt;/thead&gt;
                 &lt;tbody&gt;
                        {rows}
                 &lt;/tbody&gt;

             &lt;/table&gt;
        )
    }
    }

At the end, **my table has the following structure** 

[![enter image description here][1]][1]


  [1]: http://i.stack.imgur.com/ebTAe.png


Where is the problem?

React: validateDOMNesting: #text cannot appear as a child of &lt;tr&gt;

I am using PyCharm (Python 3) to write a Python function which accepts a dictionary as an argument with `attachment={}`.

    def put_object(self, parent_object, connection_name, **data):
        ...

    def put_wall_post(self, message, attachment={}, profile_id=&quot;me&quot;):
        return self.put_object(profile_id, &quot;feed&quot;, message=message, **attachment)

In the IDE, `attachment={}` is colored yellow. Moving the mouse over it shows a warning.

&gt; **Default arguments value is mutable**
&gt; 
&gt; This inspection detects when a mutable value as list or dictionary is
&gt; detected in a default value for an argument.
&gt; 
&gt; Default argument values are evaluated only once at function definition
&gt; time, which means that modifying the default value of the argument
&gt; will affect all subsequent calls of the function.

What does this mean and how can I resolve it?


Content Type	Original Author	Original Content on Stackoverflow
Question	Chris Fonnesbeck	View Question on Stackoverflow
Solution 1 - Python	joshterrell805	View Answer on Stackoverflow
Solution 2 - Python	Zakum	View Answer on Stackoverflow

Eliminating warnings from scikit-learn

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

Pointer is missing a nullability type specifier

Animating backgroundColor in React Native

Attributions