Models

class hhpy.modelling.Models(*args, name: str = None, df: pandas.core.frame.DataFrame = None, X_ref: Union[Sequence[T_co], str] = None, y_ref: Union[Sequence[T_co], str] = None, scaler_X: object = None, scaler_y: object = None, printf: Callable = <function tprint>)

Bases: hhpy.modelling._BaseModel

Collection of Models that allow for fitting and predicting with multiple Models at once, comparing accuracy and creating Ensembles

Parameters:

• args – multiple Model objects that will form a Models Collection
• name – name of the collection
• df – Pandas DataFrame containing the training and testing data. Can be saved to the Model object or supplied on an as needed basis.
• X_ref – List of features (predictors) used for training the model
• y_ref – List of labels (targets) to be predicted
• scaler_X – Scalar object that implements .transform and .inverse_transform, applied to the features (predictors)before training and inversely after predicting
• scaler_y – Scalar object that implements .transform and .inverse_transform, applied to the labels (targets)before training and inversely after predicting
• printf – print function to use for logging

Methods Summary

fit(fit_type, do_print)	fit all Model objects in collection
k_split(**kwargs)	apply hhpy.ds.k_split to self to create train-test or k-cross ready data
model_by_name(name, str])	extract a list of Models from the collection by their names
predict([X, df, return_type, ensemble, do_print])	predict with all models in collection
score(return_type, pivot, do_print, …)	calculate score of the Model predictions
scoreplot([x, y, hue, hue_order, row, …])	plot the score(s) using sns.barplot
train(df, k, groupby, str] = None, sortby, …)	wrapper method that combined k_split, train, predict and score

Methods Documentation

fit(fit_type: str = 'train_test', do_print: bool = True)

fit all Model objects in collection

Parameters:

• fit_type – one of [‘train_test’, ‘k_cross’, ‘final’]
• do_print – Whether to print the steps to console

Returns:

None

k_split(**kwargs)

apply hhpy.ds.k_split to self to create train-test or k-cross ready data

Parameters:kwargs – keyword arguments passed to hhpy.ds.k_split Returns:None

model_by_name(name: Union[list, str]) → Union[hhpy.modelling.Model, list]

extract a list of Models from the collection by their names

Parameters:name – name of the Model Returns:list of Models

predict(X=None, df=None, return_type='self', ensemble=False, do_print=True)

predict with all models in collection

Parameters:

• X – The feature (predictor) data used for predicting as DataFrame, np.array or column names
• df – Pandas DataFrame containing the predict data, optional if array like data is passed for X_predict
• return_type – one of [‘y’, ‘df’, ‘DataFrame’, ‘self’]
• ensemble – if True also predict with Ensemble like combinations of models. If True or mean calculatemean of individual predictions. If median calculate median of individual predictions.
• do_print – Whether to print the steps to console

Returns:

if return_type is self: None, else see Model.predict

score(return_type: str = 'self', pivot: bool = False, do_print: bool = True, display_score: bool = True, **kwargs) → Optional[pandas.core.frame.DataFrame]

calculate score of the Model predictions

Parameters:

• return_type – one of [‘self’, ‘df’, ‘DataFrame’]
• pivot – whether to pivot the DataFrame for easier readability [optional]
• do_print – Whether to print the steps to console
• display_score – Whether to display the score DataFrame
• kwargs – other keyword arguments passed to :func: ~hhpy.ds.df_score

Returns:

if return_type is ‘self’: None, else: pandas DataFrame containing the scores

scoreplot(x='y_ref', y='value', hue='model', hue_order=None, row='score', row_order=None, palette=['xkcd:blue', 'xkcd:red', 'xkcd:green', 'xkcd:cyan', 'xkcd:magenta', 'xkcd:golden yellow', 'xkcd:dark cyan', 'xkcd:red orange', 'xkcd:dark yellow', 'xkcd:easter green', 'xkcd:baby blue', 'xkcd:light brown', 'xkcd:strong pink', 'xkcd:light navy blue', 'xkcd:deep blue', 'xkcd:deep red', 'xkcd:ultramarine blue', 'xkcd:sea green', 'xkcd:plum', 'xkcd:old pink', 'xkcd:lawn green', 'xkcd:amber', 'xkcd:green blue', 'xkcd:yellow green', 'xkcd:dark mustard', 'xkcd:bright lime', 'xkcd:aquamarine', 'xkcd:very light blue', 'xkcd:light grey blue', 'xkcd:dark sage', 'xkcd:dark peach', 'xkcd:shocking pink'], width=16, height=4.5, scale=None, query=None, return_fig_ax=False, **kwargs) → Optional[tuple]

plot the score(s) using sns.barplot

Parameters:

• x – Name of the x variable in data or vector data
• y – Name of the y variable in data or vector data
• hue – Further split the plot by the levels of this variable [optional]
• hue_order – Either a string describing how the (hue) levels or to be ordered or an explicit list of levels to beused for plotting. Accepted strings are:

• sorted: following python standard sorting conventions (alphabetical for string, ascending for value)
• inv: following sort of python standard sorting conventions but in inverse order
• count: sorted by value counts
• mean, mean_ascending, mean_descending: sorted by mean value, defaults to descending
• median, mean_ascending, median_descending: sorted by median value, defaults to descending

Parameters:

• row – the variable to wrap around the rows [optional]
• row_order – Either a string describing how the (hue) levels or to be ordered or an explicit list of levels to beused for plotting. Accepted strings are:

• sorted: following python standard sorting conventions (alphabetical for string, ascending for value)
• inv: following sort of python standard sorting conventions but in inverse order
• count: sorted by value counts
• mean, mean_ascending, mean_descending: sorted by mean value, defaults to descending
• median, mean_ascending, median_descending: sorted by median value, defaults to descending

Parameters:

• palette – Collection of colors to be used for plotting. Can be a dictionary for with names for each level or a list of colors or an individual color name. Must be valid colors known to pyplot [optional]
• width – Width of each individual subplot [optional]
• height – Height of each individual subplot [optional]
• scale – scale the values [optional
• query – query to be passed to pd.DataFrame.query before plotting [optional]
• return_fig_ax – Whether to return the figure and axes objects as tuple to be captured as fig,ax = …, If False pyplot.show() is called and the plot returns None [optional]
• kwargs – other keyword arguments passed to sns.barplot

Returns:

see return_fig_ax

train(df: pandas.core.frame.DataFrame = None, k: int = 5, groupby: Union[Sequence[T_co], str] = None, sortby: Union[Sequence[T_co], str] = None, random_state: int = None, fit_type: str = 'train_test', ensemble: bool = False, scores: Union[Sequence[T_co], str] = None, scale: float = None, do_predict: bool = True, do_score: bool = True, do_split: bool = True, do_fit: bool = True, do_print: bool = True, display_score: bool = True)

wrapper method that combined k_split, train, predict and score

Parameters:

• df – Pandas DataFrame containing the training and testing data. Can be saved to the Model object or supplied on an as needed basis.
• k – see hhpy.ds.k_split see hhpy.ds.k_split
• groupby – see hhpy.ds.k_split
• sortby – see hhpy.ds.k_split
• random_state – see hhpy.ds.k_split
• fit_type – see .fit
• ensemble – if True also predict with Ensemble like combinations of models. If True or mean calculatemean of individual predictions. If median calculate median of individual predictions.
• scores – see .score
• scale – see .score
• do_print – Whether to print the steps to console
• display_score – Whether to display the score DataFrame
• do_split – whether to apply k_split [optional]
• do_fit – whether to fit the Models [optional]
• do_predict – whether to add predictions to DataFrame [optional]
• do_score – whether to create self.df_score [optional]

Returns:

None