Changes to be committed:

Added the data utils and used datasets

Author: samanemami

DataUtils/DataLoader.py

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+import numpy as np
+import pandas as pd
+from scipy.io import loadmat
+
+from icecream import ic
+
+def _load_dataset_school(data_dir):
+        """Load dataframe from csv for school."""
+
+        path = '{}/school/'.format(data_dir)        
+        df_data = pd.read_csv('{}/school_data.csv'.format(path), index_col=0)
+        df_target = pd.read_csv('{}/school_target.csv'.format(path), index_col=0)
+
+        return df_data, df_target
+
+def _load_dataset_landmine(data_dir):
+    """Load dataframe from csv for landmine."""
+
+    path = '{}/landmine/'.format(data_dir)
+
+    df_data = pd.read_csv('{}/landmine_data.csv'.format(path), index_col=0)
+    df_target = pd.read_csv('{}/landmine_target.csv'.format(path), index_col=0)
+
+    return df_data, df_target
+
+class DataLoader:
+    """
+    A class for loading popular datasets from scikit-learn library.
+    
+    Parameters
+    ----------
+    dataset_name : str, default: 'iris'
+        The name of the dataset to load. Can be 'iris', 'digits'
+    
+    Attributes
+    ----------
+    data : array-like
+        The data of the dataset
+    target : array-like
+        The target values of the dataset
+    feature_names : array-like
+        The feature names of the dataset
+    target_names : array-like
+        The target names of the dataset
+    DESCR : str
+        The description of the dataset
+        
+    """
+    def __init__(self, data_dir=None):
+        self.data_dir = data_dir
+
+    
+    
+    def load_dataset_school(self):
+        ic(self.data_dir)
+        df_data, df_target = _load_dataset_school(data_dir=self.data_dir)
+        ic(df_data)
+        return df_data.values, df_target.values
+    
+    def load_dataset_landmine(self):
+        ic(self.data_dir)
+        df_data, df_target = _load_dataset_landmine(data_dir=self.data_dir)
+        ic(df_target)
+        ic(np.unique(df_target, return_counts=True))
+        # ic(df_data)
+        return df_data.values, df_target.values
+        
+        
+    def load_dataset(self, dataset_name, **kwargs):
+        try: 
+            load_function = getattr(self, 'load_dataset_{}'.format(dataset_name))
+            df_data, df_target = load_function(**kwargs)
+        except AttributeError as e:
+            print(e)
+            # print(f"Invalid dataset_name: {dataset_name}.")
+            raise(AttributeError(f"Invalid dataset_name: {dataset_name}."))
+        return df_data, df_target

DataUtils/__init__.py

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+from .DataLoader import DataLoader
+
+__all__ = ["DataLoader"]

Datasets/adult/adult.data

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+| This data was extracted from the census bureau database found at
+| http://www.census.gov/ftp/pub/DES/www/welcome.html
+| Donor: Ronny Kohavi and Barry Becker,
+|        Data Mining and Visualization
+|        Silicon Graphics.
+|        e-mail: [email protected] for questions.
+| Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).
+| 48842 instances, mix of continuous and discrete    (train=32561, test=16281)
+| 45222 if instances with unknown values are removed (train=30162, test=15060)
+| Duplicate or conflicting instances : 6
+| Class probabilities for adult.all file
+| Probability for the label '>50K'  : 23.93% / 24.78% (without unknowns)
+| Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)
+|
+| Extraction was done by Barry Becker from the 1994 Census database.  A set of
+|   reasonably clean records was extracted using the following conditions:
+|   ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
+|
+| Prediction task is to determine whether a person makes over 50K
+| a year.
+|
+| First cited in:
+| @inproceedings{kohavi-nbtree,
+|    author={Ron Kohavi},
+|    title={Scaling Up the Accuracy of Naive-Bayes Classifiers: a
+|           Decision-Tree Hybrid},
+|    booktitle={Proceedings of the Second International Conference on
+|               Knowledge Discovery and Data Mining},
+|    year = 1996,
+|    pages={to appear}}
+|
+| Error Accuracy reported as follows, after removal of unknowns from
+|    train/test sets):
+|    C4.5       : 84.46+-0.30
+|    Naive-Bayes: 83.88+-0.30
+|    NBTree     : 85.90+-0.28
+|
+|
+| Following algorithms were later run with the following error rates,
+|    all after removal of unknowns and using the original train/test split.
+|    All these numbers are straight runs using MLC++ with default values.
+|
+|    Algorithm               Error
+| -- ----------------        -----
+| 1  C4.5                    15.54
+| 2  C4.5-auto               14.46
+| 3  C4.5 rules              14.94
+| 4  Voted ID3 (0.6)         15.64
+| 5  Voted ID3 (0.8)         16.47
+| 6  T2                      16.84
+| 7  1R                      19.54
+| 8  NBTree                  14.10
+| 9  CN2                     16.00
+| 10 HOODG                   14.82
+| 11 FSS Naive Bayes         14.05
+| 12 IDTM (Decision table)   14.46
+| 13 Naive-Bayes             16.12
+| 14 Nearest-neighbor (1)    21.42
+| 15 Nearest-neighbor (3)    20.35
+| 16 OC1                     15.04
+| 17 Pebls                   Crashed.  Unknown why (bounds WERE increased)
+|
+| Conversion of original data as follows:
+| 1. Discretized agrossincome into two ranges with threshold 50,000.
+| 2. Convert U.S. to US to avoid periods.
+| 3. Convert Unknown to "?"
+| 4. Run MLC++ GenCVFiles to generate data,test.
+|
+| Description of fnlwgt (final weight)
+|
+| The weights on the CPS files are controlled to independent estimates of the
+| civilian noninstitutional population of the US.  These are prepared monthly
+| for us by Population Division here at the Census Bureau.  We use 3 sets of
+| controls.
+|  These are:
+|          1.  A single cell estimate of the population 16+ for each state.
+|          2.  Controls for Hispanic Origin by age and sex.
+|          3.  Controls by Race, age and sex.
+|
+| We use all three sets of controls in our weighting program and "rake" through
+| them 6 times so that by the end we come back to all the controls we used.
+|
+| The term estimate refers to population totals derived from CPS by creating
+| "weighted tallies" of any specified socio-economic characteristics of the
+| population.
+|
+| People with similar demographic characteristics should have
+| similar weights.  There is one important caveat to remember
+| about this statement.  That is that since the CPS sample is
+| actually a collection of 51 state samples, each with its own
+| probability of selection, the statement only applies within
+| state.
+
+
+>50K, <=50K.
+
+age: continuous.
+workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
+fnlwgt: continuous.
+education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
+education-num: continuous.
+marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
+occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
+relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
+race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
+sex: Female, Male.
+capital-gain: continuous.
+capital-loss: continuous.
+hours-per-week: continuous.
+native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.