eflow.model_analysis.regression_analysis

Functions

check_if_directory_exists(directory_path)	Checks if the given directory path exists. Raises an error if doesn’t
convert_to_filename(filename[, …])	Attempts to make the filename string valid.
correct_directory_path(directory_path)	Attempts to convert the directory path to a proper one by removing
create_dir_structure(directory_path, …)	Creates required directory structures inside the parent
create_plt_png
create_unique_directory(directory_path, …)	Creates a unique folder in the proper directory structure.
df_to_image(df, directory_path, sub_dir, …)
dict_to_json_file(dict_obj, directory_path, …)	Writes a dict to a json file.
display(*objs[, include, exclude, metadata, …])	Display a Python object in all frontends.
explained_variance_score(y_true, y_pred[, …])	Explained variance regression score function
get_all_directories_from_path(directory_path)	Gets directories names with the provided path.
get_all_files_from_path(directory_path[, …])	Gets all filenames with the provided path.
get_unique_directory_path(directory_path, …)	Iterate through directory structure until a unique folder name can be
json_file_to_dict(filepath)	Returns back the dictionary from of a json file.
load_pickle_object(file_path)
max_error
mean_absolute_error(y_true, y_pred[, …])	Mean absolute error regression loss
mean_squared_error(y_true, y_pred[, …])	Mean squared error regression loss
mean_squared_log_error(y_true, y_pred[, …])	Mean squared logarithmic error regression loss
median_absolute_error(y_true, y_pred[, …])	Median absolute error regression loss
pickle_object_to_file(obj, directory_path, …)	Writes the object to a pickle file.
r2_score(y_true, y_pred[, sample_weight, …])	R^2 (coefficient of determination) regression score function.
write_object_text_to_file(obj, …[, …])	Writes the object’s string representation to a text file.

Classes

FeatureAnalysis(df_features[, …])	Analyzes the feature data of a pandas Dataframe object.
GRAPH_DEFAULTS
ModelAnalysis(dataset_name[, …])	All objects in model_analysis folder of eflow are related to this object.
RegressionAnalysis(dataset_name, model, …)	Analyzes a classification model’s result’s based on the prediction function(s) passed to it.

Exceptions

ProbasNotPossible
RequiresPredictionMethods
UnsatisfiedRequirments

class RegressionAnalysis(dataset_name, model, model_name, feature_order, target_feature, pred_funcs_dict, df_features, project_sub_dir='Regression Analysis', overwrite_full_path=None, save_model=True, notebook_mode=False)

Analyzes a classification model’s result’s based on the prediction function(s) passed to it. Creates graphs and tables to be saved in directory structure.

perform_analysis(X, y, dataset_name, regression_error_analysis=False, regression_correct_analysis=False, ignore_metrics=[], custom_metrics_dict={}, display_visuals=True, mse_score=None)

Runs all available analysis functions on the models predicted data.

Args:

X:

Feature matrix.

y:

Target data vector.

dataset_name:

The dataset’s name; this will create a sub-directory in which your generated graph will be inner-nested in.

regression_error_analysis: bool

Perform feature analysis on data that was incorrectly predicted.

regression_correct_analysis: bool

Perform feature analysis on data that was correctly predicted.

ignore_metrics:

Specify the default metrics to not apply to the classification data_analysis.

custom_metrics_dict:

Pass the name of metric(s) with the function definition(s) in a dictionary.

display_visuals:

Controls visual display of error error data_analysis if it is able to run.

Returns:

Performs all classification functionality with the provided feature data and target data.

• plot_precision_recall_curve

• classification_evaluation

• plot_confusion_matrix

regression_correct_analysis(X, y, pred_name, dataset_name, mse_score, display_visuals=True, save_file=True, display_print=True, suppress_runtime_errors=True, aggregate_target_feature=True, selected_features=None, extra_tables=True, statistical_analysis_on_aggregates=True)

Compares the actual target value to the predicted value and performs analysis of all the data.

Args:

X: np.matrix or lists of lists

Feature matrix.

y: collection object

Target data vector.

pred_name: str

The name of the prediction function in questioned stored in ‘self.__pred_funcs_dict’

dataset_name: str

The dataset’s name; this will create a sub-directory in which your generated graph will be inner-nested in.

feature_order: collection object

Features names in proper order to re-create the pandas dataframe.

display_visuals: bool

Boolean value to whether or not to display visualizations.

display_print: bool

Determines whether or not to print function’s embedded print statements.

save_file: bool

Boolean value to whether or not to save the file.

dataframe_snapshot: bool

Boolean value to determine whether or not generate and compare a snapshot of the dataframe in the dataset’s directory structure. Helps ensure that data generated in that directory is correctly associated to a dataframe.

suppress_runtime_errors: bool

If set to true; when generating any graphs will suppress any runtime errors so the program can keep running.

extra_tables: bool

When handling two types of features if set to true this will

generate any extra tables that might be helpful. Note -

These graphics may create duplicates if you already applied an aggregation in ‘perform_analysis’

aggregate_target_feature: bool

Aggregate the data of the target feature if the data is non-continuous data.

Note

In the future I will have this also working with continuous data.

selected_features: collection object of features

Will only focus on these selected feature’s and will ignore the other given features.

statistical_analysis_on_aggregates: bool

If set to true then the function ‘statistical_analysis_on_aggregates’ will run; which aggregates the data of the target feature either by discrete values or by binning/labeling continuous data.

regression_error_analysis(X, y, pred_name, dataset_name, mse_score, display_visuals=True, save_file=True, display_print=True, suppress_runtime_errors=True, aggregate_target_feature=True, selected_features=None, extra_tables=True, statistical_analysis_on_aggregates=True)

Compares the actual target value to the predicted value and performs analysis of all the data.

Args:

X: np.matrix or lists of lists

Feature matrix.

y: collection object

Target data vector.

pred_name: str

The name of the prediction function in questioned stored in ‘self.__pred_funcs_dict’

dataset_name: str

The dataset’s name; this will create a sub-directory in which your generated graph will be inner-nested in.

feature_order: collection object

Features names in proper order to re-create the pandas dataframe.

thresholds:

If the model outputs a probability list/numpy array then we apply thresholds to the ouput of the model. For classification only; will not affect the direct output of the probabilities.

display_visuals: bool

Boolean value to whether or not to display visualizations.

display_print: bool

Determines whether or not to print function’s embedded print statements.

save_file: bool

Boolean value to whether or not to save the file.

dataframe_snapshot: bool

Boolean value to determine whether or not generate and compare a snapshot of the dataframe in the dataset’s directory structure. Helps ensure that data generated in that directory is correctly associated to a dataframe.

suppress_runtime_errors: bool

If set to true; when generating any graphs will suppress any runtime errors so the program can keep running.

extra_tables: bool

When handling two types of features if set to true this will

generate any extra tables that might be helpful. Note -

These graphics may create duplicates if you already applied an aggregation in ‘perform_analysis’

aggregate_target_feature: bool

Aggregate the data of the target feature if the data is non-continuous data.

Note

In the future I will have this also working with continuous data.

selected_features: collection object of features

Will only focus on these selected feature’s and will ignore the other given features.

statistical_analysis_on_aggregates: bool

If set to true then the function ‘statistical_analysis_on_aggregates’ will run; which aggregates the data of the target feature either by discrete values or by binning/labeling continuous data.

regression_metrics(X, y, pred_name, dataset_name, display_visuals=True, save_file=True, title='', custom_metrics_dict={}, ignore_metrics=[], multioutput=[None, 'uniform_average', 'variance_weighted'])

Creates a dataframe based on the prediction metrics of the feature matrix and target vector.

Args:

X:

Feature matrix.

y:

Target data vector.

pred_name:

The name of the prediction function in questioned stored in ‘self.__pred_funcs_dict’

dataset_name:

The dataset’s name; this will create a sub-directory in which your generated graph will be inner-nested in.

thresholds:

If the model outputs a probability list/numpy array then we apply thresholds to the ouput of the model. For classification only; will not affect the direct output of the probabilities.

display_visuals:

Display tables.

save_file:

Determines whether or not to save the generated document.

title:

Adds to the column ‘Metric Score’.

custom_metrics_dict:

Pass the name of metric(s) and the function definition(s) in a dictionary.

ignore_metrics:

Specify the default metrics to not apply to the classification data_analysis.

• Precision

• MCC

• Recall

• F1-Score

• Accuracy

average_scoring:

Determines the type of averaging performed on the data.

• micro

• macro

• weighted

Returns:

Return a dataframe object of the metrics value.