wcs

wcs training tools

for Google Colaboratory

install and import:

!pip install "http://github.com/raoulvm/wcs/archive/main.zip" --upgrade>/dev/null
# e.g.: import google drive share helper
from wcs.google import google_drive_share

contains

Google Tools

wcs.google.google_drive_share

Helps with loading files from google drive links. See docString

Pandas Tools

wcs.pd.df_more_info(dataframe)

More than df.info(), less than pandas_profiling. Just between.

wcs.pd.fuzzyrightjoin

Lookup-join supporting function based min/max/threshold conditions. See DocString!

wcs.kraus.build_histograms_with_target(df: pd.DataFrame, target_col: str, cont_cols: List[str], ord_cols: List[str], cat_cols: List[str], pred_col: str = None, perc_winsor: float = 1, error:str=None, fix_ratio_scale:Union[float,NoneType]=1, as_pdf_name:str=None)

Clemens' helper to plot features against binary targets.

SciKit Learn Tools

wcs.skl.metrics.confusion(y_true, y_predict, [labels], [textlables], [title])

Shorthand for wcs.skl.metrics.pretty_confusionmatrix() with an inner sklearn.metrics.confusion_matrix(), with auto sorting the POSITIVE label to be 1 and the negative label to be 0, if the values support this assumption.

wcs.skl.metrics.pretty_confusionmatrix()

can print nicer explainable confusion matrices. pass it a confusion matrix and enjoy. Work In Progress Warning
new location since v. 0.0.17

pretty_confusionmatrix(confusionmatrix: np.ndarray, textlabels:List[str]=['Positive','Negative'], title:str='Confusion Matrix', texthint:str='', metrics:bool=True)->Union[object, dict]:
    """Create a more readable HTML based confusion matrix, based on sklearn 

    Args:
        confusionmatrix (np.ndarray): a sklearn.metrics.confusionmatrix  
        textlabels (List[str], optional): The class labels as list of strings. 
            Defaults to ['Positive','Negative'].  
        title (str, optional): The confusion matrix' title. Defaults to 'Confusion Matrix'.
        texthint (str, optional): Text to print in the top left corner. Defaults to ''. 
            If an empty string (default) is passed, print the population number.  
        metrics (bool, optional): Print the confusion matrix immediately, and return a 
            dict with the metrices. Defaults to True. If set to False, the function 
                returns the confusion metrix as HTMLTable object.

    Returns:
        Union[HTMLTable, dict]: The matrix as HTMLTable if `metrics` is set to False, a dict with the metrics otherwise (Default)
    """

wcs.skl.compose.get_feature_names()

returns the output columns of e.g. a column transformer with nested pipelines

wcs.skl.compose.repipe_transformer_tuples()

collates transformations for the same columns into Pipelines. See DocString Caveat: Do not use if you have transformations that require multiple columns to be passed at once! The "re-piper" will break them into multiple calls, for each column one call.

wcs.skl.preprocessing.Winsor()

Winsorization Transformer, supports fit() and tranform() compatible to other sklearn transformers.

wcs.skl.compose.make_transformer_list(tlist:list, withnames:bool=True)->list:

instantiates transformers for multiple use of the transformation list without the need of resetting them again

wcs.skl.model_selection.train_test_split()

wraps sklearn.model_selection.train_test_split() so the indices get all reset before returning the data

wcs.skl.rcat()

Mass fuer die "Korrelation" zwischen einer numerischen und einer kategoriellen Variable

Die Aufteilung in $n$ Teilmengen erfolgt anhand einer zweiten, kategoriellen Variable. Aussage: Um wieviel nimmt die Varianz ab, wenn ich die kontinuierliche Variable anhand der kategoriellen in einzelne Gruppen zerlege? Achtung, das Mass ist nicht symmetrisch!

wcs.skl.rwcat()

Wie rcat(), aber mit Gewichtung der Varianzen durch die Gruppengroessen (weniger Ausreisser-empfindlich)

NumPy tools

wcs.np.print_matrix

nicer printout of 1 and 2-dimensional matrices in colab, can also print some matrix properties. See DocString

wcs.np.geodistances.haversine_df(dataframe)

Calculates haversine based great circle distances on a dataframe. Dataframe has to have four columns: lon1, lat1, lon2, lat2 (names do not matter, it is iloc[] based)

wcs.np.geodistances.great_circle_distance(lon1, lat1, lon2, lat2)

Calculates haversine based great circle distances on two sets of longitude/latitude

Seaborn, MatplotLib and Bokeh tools

wcs.sns.corrheatmap

Print a correlation heatmap (Pearsons) from a dataframe. Defaults to a symmtric black-white-black scale with white being at 0 correlation.

def corrheatmap(data:pd.core.frame.DataFrame, 
                vmax:float=1.0,
                diagonal:bool=False,
                decimals:int=2,
                title:str='Correlation Matrix',
                colors:List[str]=['black', 'white', 'black'],
                annot:bool = True,
                as_figure:bool=True,
                figure_params:Dict[Any,Any]={'figsize':(14,8), 'dpi':75},
                )

wcs.bokeh.Geoplot

Easily create a simple interactive geo plot from lat/lon coordinates without fighting the windmills axes.

Miscellaneous

wcs.tools.pltfct

Easily plot a function

wcs.tools.HTMLtable

Create and modify text tables for display in Colab. Work In Progress Warning

to be continued...