#!/usr/bin/env python
# Created by "Thieu" at 23:33, 10/08/2023 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import pandas as pd
import numpy as np
from pathlib import Path
from metaperceptron.helpers.scaler_util import DataTransformer
from sklearn.model_selection import train_test_split
[docs]class LabelEncoder:
"""
Encode categorical features as integer labels.
"""
def __init__(self):
self.unique_labels = None
self.label_to_index = {}
[docs] @staticmethod
def check_y(y):
y = np.squeeze(np.asarray(y))
if y.ndim != 1:
raise ValueError("y label should have shape like 1-D vector.")
return y
[docs] def fit(self, y):
"""
Fit label encoder to a given set of labels.
Parameters:
-----------
y : array-like
Labels to encode.
"""
y = self.check_y(y)
self.unique_labels = np.unique(y)
self.label_to_index = {label: i for i, label in enumerate(self.unique_labels)}
[docs]class TimeSeriesDifferencer:
def __init__(self, interval=1):
if interval < 1:
raise ValueError("Interval for differencing must be at least 1.")
self.interval = interval
[docs] def difference(self, X):
self.original_data = X.copy()
return np.array([X[i] - X[i - self.interval] for i in range(self.interval, len(X))])
[docs] def inverse_difference(self, diff_data):
if self.original_data is None:
raise ValueError("Original data is required for inversion.")
return np.array([diff_data[i - self.interval] + self.original_data[i - self.interval] for i in range(self.interval, len(self.original_data))])
[docs]class FeatureEngineering:
def __init__(self):
"""
Initialize the FeatureEngineering class
"""
# Check if the threshold is a valid number
pass
[docs] def create_threshold_binary_features(self, X, threshold):
"""
Perform feature engineering to add binary indicator columns for values below the threshold.
Add each new column right after the corresponding original column.
Args:
X (numpy.ndarray): The input 2D matrix of shape (n_samples, n_features).
threshold (float): The threshold value for identifying low values.
Returns:
numpy.ndarray: The updated 2D matrix with binary indicator columns.
"""
# Check if X is a NumPy array
if not isinstance(X, np.ndarray):
raise ValueError("Input X should be a NumPy array.")
# Check if the threshold is a valid number
if not (isinstance(threshold, int) or isinstance(threshold, float)):
raise ValueError("Threshold should be a numeric value.")
# Create a new matrix to hold the original and new columns
X_new = np.zeros((X.shape[0], X.shape[1] * 2))
# Iterate over each column in X
for idx in range(X.shape[1]):
feature_values = X[:, idx]
# Create a binary indicator column for values below the threshold
indicator_column = (feature_values < threshold).astype(int)
# Add the original column and indicator column to the new matrix
X_new[:, idx * 2] = feature_values
X_new[:, idx * 2 + 1] = indicator_column
return X_new
[docs]class Data:
"""
The structure of our supported Data class
Parameters
----------
X : np.ndarray
The features of your data
y : np.ndarray
The labels of your data
"""
SUPPORT = {
"scaler": list(DataTransformer.SUPPORTED_SCALERS.keys())
}
def __init__(self, X=None, y=None, name="Unknown"):
self.X = X
self.y = self.check_y(y)
self.name = name
self.X_train, self.y_train, self.X_test, self.y_test = None, None, None, None
[docs] @staticmethod
def check_y(y):
if y is None:
return y
y = np.squeeze(np.asarray(y))
if y.ndim == 1:
y = np.reshape(y, (-1, 1))
return y
[docs] @staticmethod
def scale(X, scaling_methods=('standard', ), list_dict_paras=None):
X = np.squeeze(np.asarray(X))
if X.ndim == 1:
X = np.reshape(X, (-1, 1))
if X.ndim >= 3:
raise TypeError(f"Invalid X data type. It should be array-like with shape (n samples, m features)")
scaler = DataTransformer(scaling_methods=scaling_methods, list_dict_paras=list_dict_paras)
data = scaler.fit_transform(X)
return data, scaler
[docs] @staticmethod
def encode_label(y):
y = np.squeeze(np.asarray(y))
if y.ndim != 1:
raise TypeError(f"Invalid y data type. It should be a vector / array-like with shape (n samples,)")
scaler = LabelEncoder()
data = scaler.fit_transform(y)
return data, scaler
[docs] def split_train_test(self, test_size=0.2, train_size=None,
random_state=41, shuffle=True, stratify=None, inplace=True):
"""
The wrapper of the split_train_test function in scikit-learn library.
"""
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.X, self.y, test_size=test_size,
train_size=train_size, random_state=random_state, shuffle=shuffle, stratify=stratify)
if not inplace:
return self.X_train, self.X_test, self.y_train, self.y_test
[docs] def set_train_test(self, X_train=None, y_train=None, X_test=None, y_test=None):
"""
Function use to set your own X_train, y_train, X_test, y_test in case you don't want to use our split function
Parameters
----------
X_train : np.ndarray
y_train : np.ndarray
X_test : np.ndarray
y_test : np.ndarray
"""
self.X_train = X_train
self.y_train = y_train
self.X_test = X_test
self.y_test = y_test
return self
[docs]def get_dataset(dataset_name):
"""
Helper function to retrieve the data
Parameters
----------
dataset_name : str
Name of the dataset
Returns
-------
data: Data
The instance of Data class, that hold X and y variables.
"""
dir_root = f"{Path(__file__).parent.parent.__str__()}/data"
list_path_reg = Path(f"{dir_root}/reg").glob("*.csv")
list_path_cls = Path(f"{dir_root}/cls").glob("*.csv")
reg_list = [pf.name[:-4] for pf in list_path_reg]
cls_list = [pf.name[:-4] for pf in list_path_cls]
list_datasets = reg_list + cls_list
if dataset_name not in list_datasets:
print(f"EvoRBF currently does not have '{dataset_name}' data in its database....")
display = input("Enter 1 to see the available datasets: ") or 0
if display:
print("+ For classification problem. We support datasets:")
for idx, dataset in enumerate(cls_list):
print(f"\t{idx + 1}: {dataset}")
print("+ For regression problem. We support datasets:")
for idx, dataset in enumerate(reg_list):
print(f"\t{idx + 1}: {dataset}")
else:
if dataset_name in reg_list:
df = pd.read_csv(f"{dir_root}/reg/{dataset_name}.csv", header=None)
data_type = "REGRESSION"
else:
df = pd.read_csv(f"{dir_root}/cls/{dataset_name}.csv", header=None)
data_type = "CLASSIFICATION"
data = Data(np.array(df.iloc[:, 0:-1]), np.array(df.iloc[:, -1]))
print(f"Requested {data_type} dataset: {dataset_name} found and loaded!")
return data