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DATASET.py
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DATASET.py
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from typing import List, Optional, Tuple
import WARNINGS
import pandas as pd
import numpy as np
import os
from sklearn.preprocessing import MinMaxScaler
import COLORS
class Dataset:
def __init__(self):
# dataset info
self.name: str = ''
self.filepath: str = ''
self.dataframe: Optional[pd.DataFrame] = None
self.not_normalized_frame: Optional[pd.DataFrame] = None
# class information
self.class_count: int = 0
self.count_per_class: List[int] = []
self.class_names: List[str] = []
self.class_colors: List[Tuple[int, int, int, int]] = []
self.rule_regions = {}
# attribute information
self.attribute_count: int = 0
self.attribute_names: List[str] = []
self.attribute_alpha: int = 255 # for attribute slider
self.attribute_inversions: List[bool] = [] # for attribute inversion option
self.overlap_points = {}
# sample information
self.sample_count: int = 0
self.clipped_samples: np.ndarray = np.array([], dtype=float) # for line clip option
self.clear_samples: np.ndarray = np.array([], dtype=float)
self.vertex_in: np.ndarray = np.array([], dtype=float) # for vertex clip option
self.last_vertex_in: np.ndarray = np.array([], dtype=float) # for last vertex clip option
# plot information
self.plot_type: str = ''
self.positions: List[float] = []
self.overlap_indices = []
self.radial_bounds = {}
self.axis_positions: List[float] = []
self.axis_on: bool = True
self.axis_count: int = 0
self.vertex_count: int = 0
self.trace_mode: bool = False
self.coefs = []
self.fitted = False
self.active_attributes: np.ndarray = np.array([], dtype=bool)
self.active_classes: List[bool] = []
self.active_markers: List[bool] = []
self.active_sectors: List[bool] = []
self.class_order: List[int] = []
self.attribute_order: List[int] = []
self.all_arc_lengths: List[int] = []
def duplicate_last_attribute(self):
if self.dataframe is None or self.dataframe.empty:
print("DataFrame is not loaded or is empty.")
return
last_attribute = self.dataframe.columns[-2]
new_attribute = f'{last_attribute}_copy'
self.dataframe[new_attribute] = self.dataframe[last_attribute]
self.attribute_names.append(new_attribute)
self.attribute_count += 1
self.vertex_count += 1
self.active_attributes = np.append(self.active_attributes, True)
self.attribute_inversions = np.append(self.attribute_inversions, False)
# reorder DataFrame columns to ensure 'class' is the last column
cols = self.dataframe.columns.tolist()
cols.append(cols.pop(cols.index('class'))) # Move 'class' to the end
self.dataframe = self.dataframe[cols]
def reload(self):
if self.filepath:
try:
# store inversions
inversions = self.attribute_inversions
df = pd.read_csv(self.filepath)
self.load_frame(df)
# restore inversions
self.attribute_inversions = inversions
except Exception as e:
print(f"Error reloading data: {e}")
else:
print("No filepath set for reloading.")
def inject_datapoint(self, data_point: List[float], class_name: str):
self.dataframe = self.dataframe._append(pd.Series(data_point + [class_name], index=self.dataframe.columns), ignore_index=True)
self.not_normalized_frame = self.not_normalized_frame._append(pd.Series(data_point + [class_name], index=self.not_normalized_frame.columns), ignore_index=True)
self.sample_count += 1
self.count_per_class[self.class_names.index(class_name)] += 1
# update clipped_samples array with new sample
self.clipped_samples = np.append(self.clipped_samples, False)
self.clear_samples = np.append(self.clear_samples, False)
def update_coef(self, attribute_index, new_coef_value):
if 0 <= attribute_index < len(self.coefs):
self.coefs[attribute_index] = new_coef_value
def load_frame(self, df: pd.DataFrame, not_normal=None):
# put class column to end of dataframe
df.insert(len(df.columns) - 1, 'class', df.pop('class'))
# get class information
self.class_count = len(df['class'].unique())
self.class_names = df['class'].unique().tolist() # Keep unique class names in their original order
self.count_per_class = [df['class'].tolist().count(name) for name in self.class_names]
self.class_order = np.arange(0, self.class_count)
# get class colors and lower case
names = [str(name).lower() for name in self.class_names]
gen_green_red = False
for name in names:
if name in ['benign', 'malignant', 'positive', 'negative']:
gen_green_red = True
if gen_green_red:
self.class_colors = COLORS.getColors(self.class_count, [0, 0, 0], [1, 1, 1], names, benign_malignant=True).colors_array
else:
self.class_colors = COLORS.getColors(self.class_count, [0, 0, 0], [1, 1, 1], names).colors_array
# initialize arrays for class options
self.active_markers = np.repeat(True, self.class_count)
self.active_classes = np.repeat(True, self.class_count)
self.active_sectors = np.repeat(True, self.class_count)
# get attribute information
self.attribute_names = df.columns.tolist()[:-1]
self.attribute_count = len(df.columns) - 1
self.attribute_order = np.arange(0, self.attribute_count)
self.max_radial_distances = [0] * self.attribute_count
self.coefs = np.ones(self.attribute_count) * 100
self.active_attributes = np.repeat(True, self.attribute_count)
self.attribute_inversions = np.repeat(False, self.attribute_count)
# get sample information
self.sample_count = len(df.index)
# initialize arrays for clipping options
self.clipped_samples = np.repeat(False, self.sample_count)
self.clear_samples = np.repeat(False, self.sample_count)
self.vertex_in = np.repeat(False, self.sample_count)
self.last_vertex_in = np.repeat(False, self.sample_count)
# general dataframe
self.dataframe = df
if not_normal is not None:
self.not_normalized_frame = not_normal
else:
self.not_normalized_frame = df.copy()
def delete_clip(self):
"""Delete the selected samples from the dataframe."""
if self.dataframe is None or self.dataframe.empty:
print("DataFrame is not loaded or is empty.")
return
if not any(self.clipped_samples):
print("No samples selected for deletion.")
return
# Create a boolean mask for rows to be deleted
bool_clipped = np.array(self.clipped_samples, dtype=bool)
# Drop the rows from both dataframes
self.dataframe = self.dataframe.loc[~bool_clipped].reset_index(drop=True)
self.not_normalized_frame = self.not_normalized_frame.loc[~bool_clipped].reset_index(drop=True)
# Update class information
self.sample_count = len(self.dataframe.index)
self.count_per_class = [self.dataframe['class'].tolist().count(name) for name in self.class_names]
# Initialize arrays for clipping options
self.clipped_samples = np.repeat(False, self.sample_count)
self.clear_samples = np.repeat(False, self.sample_count)
self.vertex_in = np.repeat(False, self.sample_count)
self.last_vertex_in = np.repeat(False, self.sample_count)
# Preserve the current class colors mapping
class_color_mapping = dict(zip(self.class_names, self.class_colors))
# Reload the frame to ensure consistency
self.load_frame(self.dataframe, self.not_normalized_frame)
# Restore the preserved class colors mapping
self.class_colors = [class_color_mapping[class_name] for class_name in self.class_names]
def copy_clip(self):
if self.dataframe is None or self.dataframe.empty:
print("DataFrame is not loaded or is empty.")
return
if not any(self.clipped_samples):
print("No samples selected.")
return
# Ensure clipped_samples is a boolean array
bool_clipped = np.array(self.clipped_samples, dtype=bool)
# Get the clipped data
clipped_df = self.dataframe[bool_clipped].copy()
clipped_non_normalized_df = self.not_normalized_frame[bool_clipped].copy()
# Append the clipped data to the original dataframes
self.dataframe = pd.concat([self.dataframe, clipped_df], ignore_index=True)
self.not_normalized_frame = pd.concat([self.not_normalized_frame, clipped_non_normalized_df], ignore_index=True)
# Update sample count and count per class
self.sample_count = len(self.dataframe.index)
self.count_per_class = [self.dataframe['class'].tolist().count(name) for name in self.class_names]
# Initialize the expanded clipped_samples array with the newly added samples as true
new_clipped_samples = np.zeros(self.sample_count, dtype=bool)
new_clipped_samples[-len(clipped_df):] = True
# Sort the dataframe by class
self.dataframe = self.dataframe.sort_values(by='class').reset_index(drop=True)
self.not_normalized_frame = self.not_normalized_frame.sort_values(by='class').reset_index(drop=True)
# Reset previous clipped samples to false
self.clipped_samples = new_clipped_samples
self.clear_samples = np.repeat(False, self.sample_count)
self.vertex_in = np.repeat(False, self.sample_count)
self.last_vertex_in = np.repeat(False, self.sample_count)
self.positions = [self.dataframe.iloc[:, i].values for i in range(self.attribute_count)]
self.clear_samples = np.zeros(self.sample_count, dtype=bool)
self.vertex_in = np.zeros(self.sample_count, dtype=bool)
self.last_vertex_in = np.zeros(self.sample_count, dtype=bool)
def move_samples(self, move_delta: int):
"""Move the selected samples up or down in the dataframe."""
if self.dataframe is None or self.dataframe.empty:
print("DataFrame is not loaded or is empty.")
return
if not any(self.clipped_samples):
print("No samples selected.")
return
if move_delta == 0:
return
bool_clipped = np.array(self.clipped_samples, dtype=bool)
for attribute in self.attribute_names:
normalized_range = self.dataframe[attribute].max() - self.dataframe[attribute].min()
if normalized_range == 0:
continue # Skip attributes with no variation
proportional_delta = move_delta / normalized_range
# Calculate the proportional move for the normalized and not_normalized frames
if self.dataframe.loc[bool_clipped, attribute].min() + proportional_delta > 0 and self.dataframe.loc[bool_clipped, attribute].max() + proportional_delta < 1:
not_normalized_range = self.not_normalized_frame[attribute].max() - self.not_normalized_frame[attribute].min()
if not_normalized_range == 0:
continue # Skip attributes with no variation
not_normalized_proportional_delta = proportional_delta * not_normalized_range
self.not_normalized_frame[attribute] = self.not_normalized_frame[attribute].astype(float)
self.dataframe.loc[bool_clipped, attribute] += proportional_delta
self.not_normalized_frame.loc[bool_clipped, attribute] += not_normalized_proportional_delta
def load_from_csv(self, filename: str):
"""Load the dataset from a CSV file."""
try:
df = pd.read_csv(filename)
self.name = os.path.basename(filename)
self.filepath = filename
self.load_frame(df)
except Exception as e:
print(f"An error occurred: {e}")
def normalize_data(self, our_range: Tuple[float, float]):
"""Normalize the data in the dataframe to the specified range."""
if self.dataframe is None or self.dataframe.empty:
print("DataFrame is not loaded or is empty.")
return self.dataframe
scaler = MinMaxScaler(our_range)
# Only normalize self.dataframe
self.dataframe[self.attribute_names] = scaler.fit_transform(self.dataframe[self.attribute_names])
return self.dataframe
def normalize_col(self, col: int, our_range: Tuple[float, float]):
"""Normalize a specific column in the dataframe to the specified range."""
scaler = MinMaxScaler(our_range)
self.dataframe[self.attribute_names[col]] = scaler.fit_transform(self.dataframe[[self.attribute_names[col]]])
return self.dataframe
def roll_clips(self, roll_dir: int):
"""Select the next sample(s) to clip"""
self.clipped_samples = list(np.roll(self.clipped_samples, roll_dir))
def roll_vertex_in(self, roll_dir: int):
"""Select the previous sample(s) to clip"""
self.vertex_in = list(np.roll(self.vertex_in, roll_dir))