gershonc / octopus-ml

A collection of handy ML and data visualization and validation tools. Go ahead and train, evaluate and validate your ML models and data with minimal effort.

Octopus-ML

Set of handy ML and data tools - starting from data exploration, visualization, pre-processing, hyper parameter tuning, modeling and all the way to final ML model evaluation

Check out the octopus-ml demo notebook on Colab

Installation

The module can be easily installed with pip:

> pip install octopus-ml

This module depends on Scikit-learn, NumPy, Pandas, TQDM, lightGBM as defualt classifier. Optionally you can get also some nice visualisations if you have Seaborn installed.

Usage

The module contains ML and Data related methods:

from octopus_ml import plot_imp, adjusted_classes, cv, cv_plot, roc_curve_plot, ...

Selected visualizations:

octopus-ml's Issues

Undocumented dependency in the showcase notebook

The example notebooks are dependent on the following libraries being installed

optuna
pandas-summary
missingno

It would be helpful to automatically install these or to document the dependency along with the relative conda install instructions in the readme file.

conda install -c conda-forge optuna
conda install -c conda-forge pandas-summary
conda install -c conda-forge missingno

showcase pandas-summary breaks

When running the example code the following snippet breaks

from pandas_summary import DataFrameSummary
dfs = DataFrameSummary(XY_df)
dfs.summary()

The Conda environment in use was set up specifically for evaluation of the octopus-ml:

octopus-ml 0.1.9 pypi_0 pypi
pandas 1.2.4 py38h60cbd38_0 conda-forge
pandas-summary 0.0.41 py_1 conda-forge

Error message:

AttributeError Traceback (most recent call last)
in
1 import missingno as msno
2 from pandas_summary import DataFrameSummary
----> 3 dfs = DataFrameSummary(XY_df)
4 # dfs.summary()

~\Miniconda3\envs\octopus\lib\site-packages\pandas_summary_init_.py in init(self, df)
25 self.df = df
26 self.length = len(df)
---> 27 self.columns_stats = self._get_stats()
28 self.corr = df.corr()
29

~\Miniconda3\envs\octopus\lib\site-packages\pandas_summary_init_.py in _get_stats(self)
87 # settings types
88 stats['types'] = ''
---> 89 columns_info = self._get_columns_info(stats)
90 for ctype, columns in columns_info.items():
91 stats.ix[columns, 'types'] = ctype

~\Miniconda3\envs\octopus\lib\site-packages\pandas_summary_init_.py in _get_columns_info(self, stats)
109 self.EXCLUDE,
110 column_info['constant'].union(column_info['bool']))
--> 111 column_info[self.TYPE_NUMERIC] = pd.Index([c for c in rest_columns
112 if common.is_numeric_dtype(self.df[c])])
113 rest_columns = self.get_columns(self.df[rest_columns], self.EXCLUDE, column_info['numeric'])

~\Miniconda3\envs\octopus\lib\site-packages\pandas_summary_init_.py in (.0)
110 column_info['constant'].union(column_info['bool']))
111 column_info[self.TYPE_NUMERIC] = pd.Index([c for c in rest_columns
--> 112 if common.is_numeric_dtype(self.df[c])])
113 rest_columns = self.get_columns(self.df[rest_columns], self.EXCLUDE, column_info['numeric'])
114 column_info[self.TYPE_DATE] = pd.Index([c for c in rest_columns

AttributeError: module 'pandas.core.common' has no attribute 'is_numeric_dtype'