annoy impute with examples#
Examples related to the AnnoyKNNImputer class
with a scikit-learn regressor (e.g., LinearRegression) instance.
# Authors: The scikit-plots developers
# SPDX-License-Identifier: BSD-3-Clause
import time
import numpy as np; np.random.seed(0) # reproducibility
import pandas as pd
from sklearn.datasets import make_classification, make_regression
from sklearn.datasets import (
load_breast_cancer as data_2_classes,
load_iris as data_3_classes,
load_digits as data_10_classes,
)
from sklearn.datasets import fetch_california_housing, load_diabetes
from sklearn.model_selection import train_test_split
https://www.geeksforgeeks.org/machine-learning/ml-credit-card-fraud-detection/ https://media.geeksforgeeks.org/wp-content/uploads/20240904104950/creditcard.csv df = pd.read_csv(”https://media.geeksforgeeks.org/wp-content/uploads/20240904104950/creditcard.csv”) df.head()
Xdi_train, Xdi_val, ydi_train, ydi_val = train_test_split(
*load_diabetes(return_X_y=True), test_size=0.25, random_state=0
)
Xca_train, Xca_val, yca_train, yca_val = train_test_split(
*fetch_california_housing(return_X_y=True), test_size=0.25, random_state=0
)
Xbc_train, Xbc_val, ybc_train, ybc_val = train_test_split(
*data_2_classes(return_X_y=True), test_size=0.25, random_state=0,
stratify=data_2_classes(return_X_y=True)[1]
)
def add_missing_values(X_full, y_full, rng=np.random.RandomState(0)):
n_samples, n_features = X_full.shape
# Add missing values in 75% of the lines
missing_rate = 0.75
n_missing_samples = int(n_samples * missing_rate)
missing_samples = np.zeros(n_samples, dtype=bool)
missing_samples[:n_missing_samples] = True
rng.shuffle(missing_samples)
missing_features = rng.randint(0, n_features, n_missing_samples)
X_missing = X_full.copy()
X_missing[missing_samples, missing_features] = np.nan
y_missing = y_full.copy()
return X_missing, y_missing
Xdi_train_miss, ydi_train_miss = add_missing_values(Xdi_train, ydi_train)
Xca_train_miss, yca_train_miss = add_missing_values(Xca_train, yca_train)
Xbc_train_miss, ybc_train_miss = add_missing_values(Xbc_train, ybc_train)
Xdi_val_miss, ydi_val_miss = add_missing_values(Xdi_val, ydi_val)
Xca_val_miss, yca_val_miss = add_missing_values(Xca_val, yca_val)
Xbc_val_miss, ybc_val_miss = add_missing_values(Xbc_val, ybc_val)
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
# To use the experimental IterativeImputer, we need to explicitly ask for it:
from sklearn.experimental import enable_iterative_imputer # noqa: F401
from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MaxAbsScaler, RobustScaler
N_SPLITS = 4
def get_score(Xt, Xv, yt, yv, imputer=None, regresion=True):
if regresion:
estimator = RandomForestRegressor(random_state=0)
scoring="neg_mean_squared_error"
else:
estimator = RandomForestClassifier(random_state=0, max_depth=6, class_weight='balanced')
scoring="neg_log_loss"
if imputer is not None:
Xt = imputer.fit_transform(Xt, yt)
Xv = imputer.transform(Xv)
estimator.fit(Xt, yt)
scores = cross_val_score(
estimator,
Xv, yv, scoring=scoring, cv=N_SPLITS
)
return scores.mean(), scores.std()
n_size = 7
x_labels = np.zeros(n_size, dtype=object)
mses_diabetes = np.zeros(n_size)
stds_diabetes = np.zeros(n_size)
mses_california = np.zeros(n_size)
stds_california = np.zeros(n_size)
mses_train = np.zeros(n_size)
stds_train = np.zeros(n_size)
time_data = np.zeros(n_size)
t0 = time.time()
mses_diabetes[0], stds_diabetes[0] = get_score(Xdi_train, Xdi_val, ydi_train, ydi_val)
mses_california[0], stds_california[0] = get_score(Xca_train, Xca_val, yca_train, yca_val)
mses_train[0], stds_train[0] = get_score(Xbc_train, Xbc_val, ybc_train, ybc_val, regresion=False)
x_labels[0] = "Full Data"
T = time.time() - t0
print(T)
time_data[0] = T
24.839556455612183
t0 = time.time()
imputer = SimpleImputer(strategy="constant", fill_value=0, add_indicator=True)
mses_diabetes[1], stds_diabetes[1] = get_score(
Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss, imputer
)
mses_california[1], stds_california[1] = get_score(
Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss, imputer
)
mses_train[1], stds_train[1] = get_score(
Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss, imputer,
regresion=False
)
x_labels[1] = "Zero\nImputation\n(constant)"
T = time.time() - t0
print(T)
time_data[1] = T
25.968689680099487
t0 = time.time()
imputer = SimpleImputer(strategy="mean", add_indicator=True)
mses_diabetes[2], stds_diabetes[2] = get_score(
Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss, imputer
)
mses_california[2], stds_california[2] = get_score(
Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss, imputer
)
mses_train[2], stds_train[2] = get_score(
Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss, imputer,
regresion=False
)
x_labels[2] = "Mean\nImputation"
T = time.time() - t0
print(T)
time_data[2] = T
28.599470853805542
t0 = time.time()
imputer = SimpleImputer(strategy="median", add_indicator=True)
mses_diabetes[3], stds_diabetes[3] = get_score(
Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss, imputer
)
mses_california[3], stds_california[3] = get_score(
Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss, imputer
)
mses_train[3], stds_train[3] = get_score(
Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss, imputer,
regresion=False
)
x_labels[3] = "Median\nImputation"
T = time.time() - t0
print(T)
time_data[3] = T
28.06083917617798
t0 = time.time()
imputer = KNNImputer(add_indicator=True)
mses_diabetes[4], stds_diabetes[4] = get_score(
Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss,
make_pipeline(RobustScaler(), MaxAbsScaler(), imputer)
)
mses_california[4], stds_california[4] = get_score(
Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss,
make_pipeline(RobustScaler(), MaxAbsScaler(), imputer)
)
mses_train[4], stds_train[4] = get_score(
Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss,
make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
regresion=False
)
x_labels[4] = "KNN\nImputation"
T = time.time() - t0
print(T)
time_data[4] = T
72.31384253501892
t0 = time.time()
imputer = IterativeImputer(add_indicator=True)
mses_diabetes[5], stds_diabetes[5] = get_score(
Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss,
make_pipeline(RobustScaler(), MaxAbsScaler(), imputer)
)
mses_california[5], stds_california[5] = get_score(
Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss,
make_pipeline(RobustScaler(), MaxAbsScaler(), imputer)
)
mses_train[5], stds_train[5] = get_score(
Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss,
make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
regresion=False
)
x_labels[5] = "Iterative\nImputation\n(BayesianRidge)"
T = time.time() - t0
print(T)
time_data[5] = T
28.80577802658081
import scikitplot as sp
sp.__version__
'0.4.0.post6'
from scikitplot.experimental import enable_annoyknn_imputer
from scikitplot.impute import AnnoyKNNImputer
# print(AnnoyKNNImputer.__doc__)
t0 = time.time()
# 'angular', 'euclidean', 'manhattan', 'hamming', 'dot'
imputer = AnnoyKNNImputer(add_indicator=True, random_state=0, weights="distance")
mses_diabetes[6], stds_diabetes[6] = get_score(
Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss,
make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
)
# imputer = AnnoyKNNImputer(add_indicator=True, random_state=0, weights="distance", initial_strategy="median", metric='euclidean', n_neighbors=430, n_trees=-1)
imputer = AnnoyKNNImputer(add_indicator=True, random_state=0, weights="distance", initial_strategy="median", metric='euclidean', n_neighbors=484)
mses_california[6], stds_california[6] = get_score(
Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss,
make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
)
imputer = AnnoyKNNImputer(add_indicator=True, random_state=0, initial_strategy="median", metric='euclidean')
mses_train[6], stds_train[6] = get_score(
Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss,
make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
regresion=False
)
x_labels[6] = "AnnoyKNN\nImputation\n(Vector Based)"
T = time.time() - t0
print(T)
time_data[6] = T
45.16613411903381
import matplotlib.pyplot as plt
mses_diabetes = np.abs(mses_diabetes) # * -1
mses_california = np.abs(mses_california)
mses_train = np.abs(mses_train)
n_bars = len(mses_diabetes)
xval = np.arange(n_bars)
colors = ["r", "g", "b", "m", "c", "orange", "olive", "gray"]
# plot diabetes results
plt.figure(figsize=(18, 6))
ax1 = plt.subplot(131)
bars1 = []
for j, td in zip(xval, time_data):
bar = ax1.barh(
j,
mses_diabetes[j],
xerr=stds_diabetes[j] / 10,
color=colors[j],
alpha=0.6,
align="center",
label=str(np.round(td, 3))
)
bars1.append(bar)
# Add bar value labels
for bar in bars1:
ax1.bar_label(bar, fmt="%.3f", label_type='center', padding=-10)
ax1.set_title("Imputation Techniques with Diabetes Data")
ax1.set_xlim(left=np.min(mses_diabetes) * 0.9, right=np.max(mses_diabetes) * 1.1)
ax1.set_yticks(xval)
ax1.set_xlabel("MSE")
ax1.invert_yaxis()
ax1.set_yticklabels(x_labels)
# plot california dataset results
ax2 = plt.subplot(132)
bars2 = []
for j, td in zip(xval, time_data):
bar = ax2.barh(
j,
mses_california[j],
xerr=stds_california[j],
color=colors[j],
alpha=0.6,
align="center",
label=str(np.round(td, 3))
)
bars2.append(bar)
# Add bar value labels
for bar in bars2:
ax2.bar_label(bar, fmt="%.5f", label_type='center', padding=-10)
ax2.set_title("Imputation Techniques with California Data")
ax2.set_yticks(xval)
ax2.set_xlabel("MSE")
ax2.invert_yaxis()
ax2.set_yticklabels([""] * n_bars)
# plot train dataset results
ax3 = plt.subplot(133)
bars3 = []
for j, td in zip(xval, time_data):
bar = ax3.barh(
j,
mses_train[j],
xerr=stds_train[j],
color=colors[j],
alpha=0.6,
align="center",
label=str(np.round(td, 3))
)
bars3.append(bar)
# Add bar value labels
for bar in bars3:
ax3.bar_label(bar, fmt="%.5f", label_type='center', padding=-10)
ax3.set_title("Imputation Techniques with Breast Cancer Data")
ax3.set_yticks(xval)
ax3.set_xlabel("LogLoss")
ax3.invert_yaxis()
ax3.set_yticklabels([""] * n_bars)
plt.legend(title='Time')
plt.tight_layout()
plt.show()
AnnoyKNNImputer performance and accuracy are highly sensitive to both the selected distance metric and the number of trees used to build the Annoy index. An inappropriate metric or insufficient number of trees may lead to poor neighbor retrieval and degraded imputation quality.
Total running time of the script: (4 minutes 15.120 seconds)
Related examples
