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dataset.py
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dataset.py
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<<<<<<< HEAD
import random
import csv
import math
from itertools import groupby
class Dataset():
def __init__(self, data, target_index, columns_names, name):
"""Method create Dataset item
Args:
data (list): list of table rows
target_index (int): index of column with target attribute
columns_names (list): list with columns names
"""
# Check data
if data is None:
raise ValueError("Data in None!")
self._data = data
# Check target_index for valid index
if target_index >= self.get_columns_number():
raise ValueError(f"Target index out of range! Got {target_index}, columns number: {self.get_columns_number()}")
self._target_index = target_index
# Check is number of columns names same as columns in data
# If not raise exception
if len(columns_names) != self.get_columns_number():
raise ValueError(f"Wrong columns names number! Got {len(columns_names)}, columns number: {self.get_columns_number()}")
self._columns_names = columns_names
self._name = name
# TODO pathlib
@staticmethod
def get_iris(path=None):
"""Method return iris dataset
by default dataset must be available
by path "resources\\data\\iris\\iris.data"
link to dataset "http://archive.ics.uci.edu/ml/datasets/Iris/"
Args:
path (string): Path to file with data
Returns:
Dataset: Dataset object based on iris data set
"""
# If path not specify
if path is None:
path = "resources\\data\\iris\\iris.data"
# Open file as csv
csv_reader = csv.reader(open(path), delimiter=",")
# Save data to list
data = []
# For each row in file
for row in csv_reader:
temp = []
# Read first 4 attributes as float
for i in range(4):
temp.append(float(row[i]))
# Read last attribute as string
temp.append(row[4])
data.append(temp)
# Set attributes names
names = [
"Sepal length", "Sepal width",
"Petal length", "Petal width",
"Class"]
# Create Dataset
dataset = Dataset(data, 4, names, "Iris")
# return Dataset
return dataset
# TODO pathlib
@staticmethod
def get_tennis(path=None):
"""Method return tennis dataset
by default dataset must be available
by path "resources\\data\\tennis\\tennis.data"
link to dataset "http://archive.ics.uci.edu/ml/datasets/Iris/"
Args:
path (string): Path to file with data
Returns:
Dataset: Dataset object based on tennis data set
"""
# If path not specify
if path is None:
path = "resources\\data\\tennis\\tennis.data"
# Open file as csv
csv_reader = csv.reader(open(path), delimiter=",")
# Save data to list
data = [row for row in csv_reader]
# Set attributes names
names = [
"Outlook", "Temperature",
"Humidity", "Wind",
"PlayTennis"]
# Create Dataset
dataset = Dataset(data, 4, names, "Tennis")
# return Dataset
return dataset
def print(self, rows_number=None):
"""Method prints the dataset in console
Args:
rows_number (int): number of rows to print
"""
# If number of rows to print not specify
if rows_number is None:
# Set number of rows in dataset
rows_number = self.get_rows_number()
# Print dataset name
print(f"Name = {self._name}")
# Print name of target attributes
print(f"Target = {self._columns_names[self._target_index]}")
# Print column columns_names
for name in self._columns_names:
# Center align of text
print("{:^15}".format(name), end=" | ")
# Go to new line
print()
# Print data as table
for i in range(min([rows_number, self.get_rows_number()])):
for attribute in self.data[i]:
# Center align of text
print("{:^15}".format(attribute), end=" | ")
# Go to new line
print()
@property
def data(self):
return [row.copy() for row in self._data]
@property
def target(self):
return self._target_index
@target.setter
def target(self, new_target_index):
# If index is valid
if self.is_column_index_correct(new_target_index):
# Set new value
self._target_index = new_target_index
@property
def name(self):
return self._name
@name.setter
def name(self, new_name):
self._name = new_name
def is_column_index_correct(self, column_index):
"""Method check is column_index is valid
Args:
column_index (int): index to check
Retuns:
false: index is invalid
true: index is valid
"""
# Check is index valid
if column_index > self.get_columns_number() or column_index < 0:
# If not valid return false
return False
# Index is valid return true
return True
def get_row(self, index):
"""Method return row by index
Args:
index (int): index of row.
Must be large than 0 and less than rows number in dataset
Returns:
list: row
"""
# Check is index valid
if index > self.get_rows_number() or index < 0:
return None
# Return copy of the row
return data[index].copy()
def get_column(self, column_index):
"""Method returns values of required column
Args:
index (int): index of column to get values from
Returns:
list: values from requested column
None: if index is incorrect
"""
# Check is index invalid
if not self.is_column_index_correct(column_index):
return None
# Return column
return [row[column_index] for row in self._data]
def get_target_column(self):
"""Method returns list of values from column with target attributes
Returns:
list: values from target attributes
"""
# Return target column
return self.get_column(self._target_index)
def get_rows_number(self):
"""Method returns number of rows in dataset
Returns:
int: number of rows
"""
return len(self._data)
def get_columns_number(self):
"""Method returns number of columns in dataset
Returns:
int: number of columns
"""
return len(self._data[0])
def get_name(self, column_index):
"""Method return column name
Args:
column_index (int): index of column to get name from
Returns:
string: name of specific column
None: if column_index is incorrect
"""
# Check is index invalid
if not self.is_column_index_correct(column_index):
return None
# Return column name
return self._columns_names[column_index]
def remove_column(self, column_index):
"""Method remove specific column from dataset
Args:
column_indes (int): index of column to remove
Returns:
None: if column_index is incorrect
or if try to remove target column
"""
# Check is index invalid
if not self.is_column_index_correct(column_index):
return None
# Check to remove not target column
if column_index == self.target:
return None
# Move target column
if column_index < self.target:
self.target -= 1
# Delete column name
self._columns_names.pop(column_index)
# In each row remove specific attribute
for row in self._data:
row.pop(column_index)
def split_by_predicate(self, column_index, predicate):
"""Method split dataset by specific column and predicate
Args:
column_index (int): index of column by which split
predicate (predicate): function by which split
takes two arguments (row (list), column_index (int))
Returns:
(list, list): first list is list of datasets, second list values of column by which split
None: if column_index is incorrect
"""
# Check is index invalid
if not self.is_column_index_correct(column_index):
return None
# Unpacking predicate
unpacking_predicate = lambda row: predicate(row, column_index)
# Group data using this predicate
datas = groupby(sorted(self.data, key=unpacking_predicate), key=unpacking_predicate)
# Separate grouped datas on key value and data
splitted_data = []
keys = []
for key, value in datas:
# Save keys values
keys.append(key)
# Convert to list
splitted_data.append(list(value))
# Convert data to Datasets objects
datasets = [
Dataset(splitted_data[i], self._target_index, self._columns_names.copy(), self._name)
for i in range(len(splitted_data))]
# Return datasets and key values
return datasets, keys
def split_by_ratio(self, ratio):
"""Method split dataset into training and test parts with given ratio
Args:
ratio (float): percentage of training part in dataset. In range [0, 1]
Returns:
tuple: consists of training and test Dataset
"""
# Calculate length of first part of dataset
TRAIN_DATASET_LEN = round(len(self._data) * ratio)
# Create list of values for first dataset
first = [
self._data[i].copy()
for i in range(TRAIN_DATASET_LEN)]
# Create list of values for second dataset
second = [
self._data[i].copy()
for i in range(TRAIN_DATASET_LEN, self.get_rows_number())]
# Create two datasets and return them
return (Dataset(first, self._target_index, self._columns_names, self._name),
Dataset(second, self._target_index, self._columns_names, self._name))
def shuffle(self):
"""Method shuffle rows in dataset
Returns:
Dataset: Dataset with shuffled rows
"""
# Get data
data = self.data
# Shuffle data
random.shuffle(data)
# Create new dataset on shuffled data
return Dataset(data, self._target_index, self._columns_names, self._name)
# TODO different variants of normalization
# for date type and etc.
def normalize(self, column_index):
"""Method normalize the specific column
Args:
column_index (int): index of column to normalize
Returns:
None: if column_index is incorrect
"""
# Check is index invalid
if not self.is_column_index_correct(column_index):
return None
# Find minimum value in column
minimum = min(self.get_column(column_index))
# Find maximum value in column
maximum = max(self.get_column(column_index))
# For each value on column
for row in self._data:
# Calculate new value
row[column_index] = (row[column_index] - minimum) / (maximum - minimum)
def threshold(self, column_index, method=None):
"""Method thresholds specific column by specific method
thresholds means change values to 0 and 1
according to threshold value
0 - less then threshold value
1 - otherwise
Args:
column_index (int): index of column to threshold
method (string): method which used to find threshold value
can ve "median" or "gain"
Returns:
threshold (float): value by which thresholds
None: if column_index is incorrect
"""
# Check is index invalid
if not self.is_column_index_correct(column_index):
return None
threshold = None
# Finding threshold acording to the method
if method is None or method == "median":
threshold = self._find_threshold_median(column_index)
elif method == "gain":
threshold = self._find_threshold_gain(column_index)
# If got unknown method
else:
return None
# Use treshold to change column values
for row in self._data:
if row[column_index] < threshold:
row[column_index] = 0
else:
row[column_index] = 1
# Return threshold value
return threshold
def _find_threshold_median(self, column_index):
"""Method return median value of the specific column
Args:
column_index (int): index of column to find median
Returns:
median (float): median value of column
None: if column_index is incorrect
"""
# Check is index invalid
if not self.is_column_index_correct(column_index):
return None
# Get column
column = self.get_column(column_index)
# Sort
column.sort()
# Find median index
med = int(self.get_rows_number() / 2)
# Return median value
return column[med]
def _find_threshold_gain(self, column_index):
"""Method return threshold of column
find using information gain
Args:
column_index (int): index of column to find threshold
Returns:
threshold (float): threshold value of column
None: if column_index is incorrect
"""
# Check is index invalid
if not self.is_column_index_correct(column_index):
return None
column = self.get_column(column_index)
target = self.get_target_column()
# Concat column and target column
# Convert to list of turples
pairs = list(zip(column, target))
# Sort pairs
pairs.sort()
# Find thresholds
thresholds = [
# Find average
(pairs[i - 1][0] + pairs[i][0]) / 2
# For each element in pairs, starting from second
for i in range(1, len(column))
# If target value changed
if pairs[i - 1][1] != pairs[i][1]]
# Remove duplicates
thresholds = list(set(thresholds))
# Calculate gain for each threshold
gains = [
Dataset.gain(
self.get_column(column_index),
self.get_target_column(),
lambda x, y: x < threshold)
for threshold in thresholds]
# Find index of max gain
index = gains.index(max(gains))
# Return thresholds with max gain
return thresholds[index]
@staticmethod
def entropy(column):
"""Method calculate column entropy
Args:
column (List): list of values
Returns:
float: entropy of column. In the range [0, 1]
None: if column is None
"""
# If column is None
if column == None:
# Return None
return None
# Group column by identity
grouped = groupby(sorted(column))
# Count rows number
rows_number = len(column)
# Count number of clases
n = len(set(column))
# If have only one class, in this case entropy equals 0
if n == 1:
# Return 0
return 0
# Calculate entropy
output = 0
# For each group
for _, value in grouped:
# Get list of values
v = list(value)
# Calculate
output -= len(v) / rows_number * math.log(len(v)/rows_number, n)
# Return entropy
return output
@staticmethod
def gain(column, target_column, predicate=None):
"""Method calculates gain of specific column
Args:
index (int): index of column
predicate (function(x, y)): function that takes two arguments,
used to group column
Return:
float: gain of column
None: if index is wrong
"""
# Concat column and target column
pairs = zip(column, target_column)
# If predicate doesn't specified
if predicate == None:
unpacking_predicate = lambda x: x[0]
# Otherwise
else:
# Unpacking predicate
unpacking_predicate = lambda x: predicate(*x)
# Group by predicate
grouped = groupby(sorted(pairs, key=unpacking_predicate), unpacking_predicate)
# Count row number
rows_number = len(column)
# Count entropy of target column
output = Dataset.entropy(target_column)
# For each group
for _, value in grouped:
# Get list of values
v = list(value)
# Calculate
output -= len(v) / rows_number * Dataset.entropy(v)
# Return entropy
return output
=======
import random
import csv
import math
from itertools import groupby
class Dataset():
def __init__(self, data, target_index, columns_names, name):
"""Method create Dataset item
Args:
data (list): list of table rows
target_index (int): index of column with target attribute
columns_names (list): list with columns names
"""
# Check data
if data is None:
raise ValueError("Data in None!")
self._data = data
# Check target_index for valid index
if target_index >= self.get_columns_number():
raise ValueError(f"Target index out of range! Got {target_index}, columns number: {self.get_columns_number()}")
self._target_index = target_index
# Check is number of columns names same as columns in data
# If not raise exception
if len(columns_names) != self.get_columns_number():
raise ValueError(f"Wrong columns names number! Got {len(columns_names)}, columns number: {self.get_columns_number()}")
self._columns_names = columns_names
self._name = name
# TODO pathlib
@staticmethod
def get_iris(path=None):
"""Method return iris dataset
by default dataset must be available
by path "resources\\data\\iris\\iris.data"
link to dataset "http://archive.ics.uci.edu/ml/datasets/Iris/"
Args:
path (string): Path to file with data
Returns:
Dataset: Dataset object based on iris data set
"""
# If path not specify
if path is None:
path = "resources\\data\\iris\\iris.data"
# Open file as csv
csv_reader = csv.reader(open(path), delimiter=",")
# Save data to list
data = []
# For each row in file
for row in csv_reader:
temp = []
# Read first 4 attributes as float
for i in range(4):
temp.append(float(row[i]))
# Read last attribute as string
temp.append(row[4])
data.append(temp)
# Set attributes names
names = [
"Sepal length", "Sepal width",
"Petal length", "Petal width",
"Class"]
# Create Dataset
dataset = Dataset(data, 4, names, "Iris")
# return Dataset
return dataset
@staticmethod
def get_tennis(path=None):
"""Method return tennis dataset
by default dataset must be available
by path "resources\\data\\tennis\\tennis.data"
link to dataset "http://archive.ics.uci.edu/ml/datasets/Iris/"
Args:
path (string): Path to file with data
Returns:
Dataset: Dataset object based on tennis data set
"""
# If path not specify
if path is None:
path = "resources\\data\\tennis\\tennis.data"
# Open file as csv
csv_reader = csv.reader(open(path), delimiter=",")
# Save data to list
data = [row for row in csv_reader]
# Set attributes names
names = [
"Outlook", "Temperature",
"Humidity", "Wind",
"PlayTennis"]
# Create Dataset
dataset = Dataset(data, 4, names, "Tennis")
# return Dataset
return dataset
def print(self, rows_number=None):
"""Method prints the dataset in console
Args:
rows_number (int): number of rows to print
"""
# If number of rows to print not specify
if rows_number is None:
# Set number of rows in dataset
rows_number = self.get_rows_number()
# Print dataset name
print(f"Name = {self._name}")
# Print name of target attributes
print(f"Target = {self._columns_names[self._target_index]}")
# Print column columns_names
for name in self._columns_names:
# Center align of text
print("{:^15}".format(name), end=" | ")
# Go to new line
print()
# Print data as table
for i in range(min([rows_number, self.get_rows_number()])):
for attribute in self.data[i]:
# Center align of text
print("{:^15}".format(attribute), end=" | ")
# Go to new line
print()
@property
def data(self):
return [row.copy() for row in self._data]
@property
def target(self):
return self._target_index
@target.setter
def target(self, new_index):
if new_index < self.get_columns_number() and new_index >= 0:
self._target_index = new_index
@property
def name(self):
return self._name
@name.setter
def name(self, new_name):
self._name = new_name
def get_row(self, index):
"""Method return row by index
Args:
index (int): index of row.
Must be large than 0 and less than rows number in dataset
Returns:
list: row
"""
# Check is index valid
if index > self.get_rows_number() or index < 0:
return None
# Return copy of the row
return data[index].copy()
def get_column(self, index):
"""Method returns values of required column
Args:
index (int): index of column to get values from
Returns:
list: values from requested column
None: if index is incorrect
"""
# Check is index valid
if index > self.get_columns_number() or index < 0:
return None
# Return column
return [row[index] for row in self._data]
def get_target_column(self):
"""Method returns list of values from column with target attributes
Returns:
list: values from target attributes
"""
return self.get_column(self._target_index)
def get_rows_number(self):
"""Method returns number of rows in dataset
Returns:
int: number of rows
"""
return len(self._data)
def get_columns_number(self):
"""Method returns number of columns in dataset
Returns:
int: number of columns
"""
return len(self._data[0])
# TODO doc
def get_name(self, column_index):
return self._columns_names[column_index]
# TODO doc
def remove_column(self, column_index):
# Check is index valid
if column_index > self.get_columns_number() or column_index < 0 or column_index == self.target:
return None
# Move target column
if column_index < self.target:
self.target -= 1
# Delete column name
self._columns_names.pop(column_index)
# In each row remove specific attribute
for row in self._data:
row.pop(column_index)
# TODO doc
def split_by_predicate(self, index, predicate):
"""
"""
# Unpacking predicate
unpacking_predicate = lambda row: predicate(row, index)
# Group data using this predicate
datas = groupby(sorted(self.data, key=unpacking_predicate), key=unpacking_predicate)
# Separate grouped datas on key value and data
splitted_data = []
keys = []
for key, value in datas:
# Save keys values
keys.append(key)
# Convert to list
splitted_data.append(list(value))
# Convert data to Datasets objects
datasets = [
Dataset(splitted_data[i], self._target_index, self._columns_names.copy(), self._name)
for i in range(len(splitted_data))]
# Return datasets and key values
return datasets, keys
def split_by_ratio(self, ratio):
"""Method split dataset into training and test parts with given ratio
Args:
ratio (float): percentage of training part in dataset. In range [0, 1]
Returns:
tuple: consists of training and test Dataset
"""
# Calculate length of first part of dataset
TRAIN_DATASET_LEN = round(len(self._data) * ratio)
# Create list of values for first dataset
first = [
self._data[i].copy()
for i in range(TRAIN_DATASET_LEN)]
# Create list of values for second dataset
second = [
self._data[i].copy()
for i in range(TRAIN_DATASET_LEN, self.get_rows_number())]
# Create two datasets and return them
return (Dataset(first, self._target_index, self._columns_names, self._name),
Dataset(second, self._target_index, self._columns_names, self._name))
def shuffle(self):
"""Method shuffle rows in dataset
Returns:
Dataset: Dataset with shuffled rows
"""
# Get data
data = self.data
# Shuffle data
random.shuffle(data)
# Create new dataset on shuffled data
return Dataset(data, self._target_index, self._columns_names, self._name)
# TODO different variants of normalization
# for date type and etc.
def normalize(self, column_index):
"""Method normalize the specific column
Args:
column_index (int): index of column to normalize
"""
# Find minimum value in column
minimum = min(self.get_column(column_index))
# Find maximum value in column
maximum = max(self.get_column(column_index))
# For each value on column
for row in self._data:
# Calculate new value
row[column_index] = (row[column_index] - minimum) / (maximum - minimum)
# TODO doc
def threshold(self, column_index, method=None):
"""
"""
threshold = None
# Finding threshold acording to the method
if method is None or method == "median":
threshold = self._find_threshold_median(column_index)
elif method == "gain":
threshold = self._find_threshold_gain(column_index)
# If got unknown method
else:
return None
# Use treshold to change column values
for row in self._data:
if row[column_index] < threshold:
row[column_index] = 0
else:
row[column_index] = 1
# Return threshold value
return threshold
# TODO doc
def _find_threshold_median(self, column_index):
"""
"""
# Get column
column = self.get_column(column_index)
# Sort
column.sort()
# Find median index
med = int(self.get_rows_number() / 2)
# Return median value
return column[med]
# TODO doc
def _find_threshold_gain(self, column_index):
"""
"""
column = self.get_column(column_index)
target = self.get_target_column()
# Concat column and target column
# Convert to list of turples
pairs = list(zip(column, target))
# Sort pairs
pairs.sort()
# Find thresholds
thresholds = [
# Find average
(pairs[i - 1][0] + pairs[i][0]) / 2
# For each element in pairs, starting from second
for i in range(1, len(column))
# If target value changed
if pairs[i - 1][1] != pairs[i][1]]
# Remove duplicates
thresholds = list(set(thresholds))
# Calculate gain for each threshold
gains = [
Dataset.gain(
self.get_column(column_index),
self.get_target_column(),
lambda x, y: x < threshold)
for threshold in thresholds]
# Find index of max gain
index = gains.index(max(gains))
# Return thresholds with max gain
return thresholds[index]
@staticmethod
def entropy(column):
"""Method calculate column entropy
Args:
column (List): list of values
Returns:
float: entropy of column. In the range [0, 1]
None: if column is None
"""
# If column is None
if column == None:
# Return None
return None
# Group column by identity
grouped = groupby(sorted(column))
# Count rows number
rows_number = len(column)
# Count number of clases
n = len(set(column))
# If have only one class, in this case entropy equals 0
if n == 1:
# Return 0
return 0
# Calculate entropy
output = 0
# For each group
for _, value in grouped:
# Get list of values
v = list(value)
# Calculate
output -= len(v) / rows_number * math.log(len(v)/rows_number, n)
# Return entropy
return output
@staticmethod
def gain(column, target_column, predicate=None):
"""Method calculates gain of specific column
Args:
index (int): index of column
predicate (function(x, y)): function that takes two arguments,
used to group column
Return:
float: gain of column
None: if index is wrong
"""
# Concat column and target column
pairs = zip(column, target_column)
# If predicate doesn't specified
if predicate == None:
unpacking_predicate = lambda x: x[0]
# Otherwise
else:
# Unpacking predicate
unpacking_predicate = lambda x: predicate(*x)
# Group by predicate
grouped = groupby(sorted(pairs, key=unpacking_predicate), unpacking_predicate)
# Count row number
rows_number = len(column)
# Count entropy of target column
output = Dataset.entropy(target_column)
# For each group
for _, value in grouped:
# Get list of values
v = list(value)
# Calculate
output -= len(v) / rows_number * Dataset.entropy(v)
# Return entropy
return output
>>>>>>> f6991836c5eab7e0917c2d1b92b3a9d555f51f31