def test_complete_features_weighted():
# Test with use_complete=True
X = np.array([[0, 0, 0, np.nan], [1, 1, 1, np.nan], [2, 2, np.nan, 2],
[3, 3, 3, 3], [4, 4, 4, 4], [5, 5, 5, 5], [6, 6, 6, 6],
[np.nan, 7, 7, 7]])
dist = pairwise_distances(X, metric="masked_euclidean", squared=False)
# Calculate weights
r0c3_w = 1.0 / dist[0, 2:-1]
r1c3_w = 1.0 / dist[1, 2:-1]
r2c2_w = 1.0 / dist[2, (0, 1, 3, 4, 5)]
r7c0_w = 1.0 / dist[7, 2:7]
# Calculate weighted averages
r0c3 = np.average(X[2:-1, -1], weights=r0c3_w)
r1c3 = np.average(X[2:-1, -1], weights=r1c3_w)
r2c2 = np.average(X[(0, 1, 3, 4, 5), 2], weights=r2c2_w)
r7c0 = np.average(X[2:7, 0], weights=r7c0_w)
X_imputed = np.array([[0, 0, 0, r0c3], [1, 1, 1, r1c3], [2, 2, r2c2, 2],
[3, 3, 3, 3], [4, 4, 4, 4], [5, 5, 5, 5],
[6, 6, 6, 6], [r7c0, 7, 7, 7]])
imputer_comp_wt = KNNImputer(weights="distance")
assert_array_almost_equal(imputer_comp_wt.fit_transform(X), X_imputed)
def test_knn_n_neighbors():
X = np.array([[0, 0], [np.nan, 2], [4, 3], [5, np.nan], [7, 7],
[np.nan, 8], [14, 13]])
statistics_mean = np.nanmean(X, axis=0)
# Test with 1 neighbor
X_imputed_1NN = np.array([[0, 0], [4, 2], [4, 3], [5, 3], [7, 7], [7, 8],
[14, 13]])
n_neighbors = 1
imputer = KNNImputer(n_neighbors=n_neighbors)
assert_array_equal(imputer.fit_transform(X), X_imputed_1NN)
assert_array_equal(imputer.statistics_, statistics_mean)
# Test with 6 neighbors
X = np.array([[0, 0], [np.nan, 2], [4, 3], [5, np.nan], [7, 7],
[np.nan, 8], [14, 13]])
X_imputed_6NN = np.array([[0, 0], [6, 2], [4, 3], [5, 5.5], [7, 7], [6, 8],
[14, 13]])
n_neighbors = 6
imputer = KNNImputer(n_neighbors=6)
imputer_plus1 = KNNImputer(n_neighbors=n_neighbors + 1)
assert_array_equal(imputer.fit_transform(X), X_imputed_6NN)
assert_array_equal(imputer.statistics_, statistics_mean)
assert_array_equal(imputer.fit_transform(X),
imputer_plus1.fit(X).transform(X))
def test_default_with_invalid_input():
# Test imputation with default values and invalid input
# Test with % missing in a column > col_max_missing
X = np.array([
[np.nan, 0, 0, 0, 5],
[np.nan, 1, 0, np.nan, 3],
[np.nan, 2, 0, 0, 0],
[np.nan, 6, 0, 5, 13],
[np.nan, 7, 0, 7, 8],
[np.nan, 8, 0, 8, 9],
])
imputer = KNNImputer()
msg = "Some column(s) have more than {}% missing values".format(
imputer.col_max_missing * 100)
assert_raise_message(ValueError, msg, imputer.fit, X)
# Test with insufficient number of neighbors
X = np.array([
[1, 1, 1, 2, np.nan],
[2, 1, 2, 2, 3],
[3, 2, 3, 3, 8],
[6, 6, 2, 5, 13],
])
msg = "There are only %d samples, but n_neighbors=%d." % \
(X.shape[0], imputer.n_neighbors)
assert_raise_message(ValueError, msg, imputer.fit, X)
# Test with inf present
X = np.array([
[np.inf, 1, 1, 2, np.nan],
[2, 1, 2, 2, 3],
[3, 2, 3, 3, 8],
[np.nan, 6, 0, 5, 13],
[np.nan, 7, 0, 7, 8],
[6, 6, 2, 5, 7],
])
msg = "+/- inf values are not allowed."
assert_raise_message(ValueError, msg, KNNImputer().fit, X)
# Test with inf present in matrix passed in transform()
X = np.array([
[np.inf, 1, 1, 2, np.nan],
[2, 1, 2, 2, 3],
[3, 2, 3, 3, 8],
[np.nan, 6, 0, 5, 13],
[np.nan, 7, 0, 7, 8],
[6, 6, 2, 5, 7],
])
X_fit = np.array([
[0, 1, 1, 2, np.nan],
[2, 1, 2, 2, 3],
[3, 2, 3, 3, 8],
[np.nan, 6, 0, 5, 13],
[np.nan, 7, 0, 7, 8],
[6, 6, 2, 5, 7],
])
msg = "+/- inf values are not allowed in data to be transformed."
assert_raise_message(ValueError, msg, KNNImputer().fit(X_fit).transform, X)
def test_knn_imputation_shape():
# Verify the shapes of the imputed matrix for different weights and
# number of neighbors.
n_rows = 10
n_cols = 2
X = np.random.rand(n_rows, n_cols)
X[0, 0] = np.nan
for weights in ['uniform', 'distance']:
for n_neighbors in range(1, 6):
imputer = KNNImputer(n_neighbors=n_neighbors, weights=weights)
X_imputed = imputer.fit_transform(X)
assert_equal(X_imputed.shape, (n_rows, n_cols))
def test_knn_imputation_zero():
# Test imputation when missing_values == 0
missing_values = 0
n_neighbors = 2
imputer = KNNImputer(missing_values=missing_values,
n_neighbors=n_neighbors,
weights="uniform")
# Test with missing_values=0 when NaN present
X = np.array([
[np.nan, 0, 0, 0, 5],
[np.nan, 1, 0, np.nan, 3],
[np.nan, 2, 0, 0, 0],
[np.nan, 6, 0, 5, 13],
])
msg = "Input contains NaN, infinity or a value too large for %r." % X.dtype
assert_raise_message(ValueError, msg, imputer.fit, X)
# Test with % zeros in column > col_max_missing
X = np.array([
[1, 0, 0, 0, 5],
[2, 1, 0, 2, 3],
[3, 2, 0, 0, 0],
[4, 6, 0, 5, 13],
])
msg = "Some column(s) have more than {}% missing values".format(
imputer.col_max_missing * 100)
assert_raise_message(ValueError, msg, imputer.fit, X)
def test_callable_metric():
# Define callable metric that returns the l1 norm:
def custom_callable(x, y, missing_values="NaN", squared=False):
x = np.ma.array(x, mask=np.isnan(x))
y = np.ma.array(y, mask=np.isnan(y))
dist = np.nansum(np.abs(x - y))
return dist
X = np.array([[4, 3, 3, np.nan], [6, 9, 6, 9], [4, 8, 6, 9],
[np.nan, 9, 11, 10.]])
X_imputed = np.array([[4, 3, 3, 9], [6, 9, 6, 9], [4, 8, 6, 9],
[5, 9, 11, 10.]])
imputer = KNNImputer(n_neighbors=2, metric=custom_callable)
assert_array_equal(imputer.fit_transform(X), X_imputed)
def test_metric_type():
X = np.array([[0, 0], [np.nan, 2], [4, 3], [5, 6], [7, 7], [9, 8],
[11, 10]])
# Test with a metric type without NaN support
imputer = KNNImputer(metric="euclidean")
bad_metric_msg = "The selected metric does not support NaN values."
assert_raise_message(ValueError, bad_metric_msg, imputer.fit, X)
def test_complete_features():
# Test with use_complete=True
X = np.array([[0, np.nan, 0, np.nan], [1, 1, 1, np.nan], [2, 2, np.nan, 2],
[3, 3, 3, 3], [4, 4, 4, 4], [5, 5, 5, 5], [6, 6, 6, 6],
[np.nan, 7, 7, 7]])
r0c1 = np.mean(X[1:6, 1])
r0c3 = np.mean(X[2:-1, -1])
r1c3 = np.mean(X[2:-1, -1])
r2c2 = np.nanmean(X[:6, 2])
r7c0 = np.mean(X[2:-1, 0])
X_imputed = np.array([[0, r0c1, 0, r0c3], [1, 1, 1, r1c3], [2, 2, r2c2, 2],
[3, 3, 3, 3], [4, 4, 4, 4], [5, 5, 5, 5],
[6, 6, 6, 6], [r7c0, 7, 7, 7]])
imputer_comp = KNNImputer()
assert_array_almost_equal(imputer_comp.fit_transform(X), X_imputed)
def test_weight_uniform():
X = np.array([[0, 0], [np.nan, 2], [4, 3], [5, 6], [7, 7], [9, 8],
[11, 10]])
# Test with "uniform" weight (or unweighted)
X_imputed_uniform = np.array([[0, 0], [5, 2], [4, 3], [5, 6], [7, 7],
[9, 8], [11, 10]])
imputer = KNNImputer(weights="uniform")
assert_array_equal(imputer.fit_transform(X), X_imputed_uniform)
# Test with "callable" weight
def no_weight(dist=None):
return None
imputer = KNNImputer(weights=no_weight)
assert_array_equal(imputer.fit_transform(X), X_imputed_uniform)
def test_knn_imputation_zero_p2():
# Test with an imputable matrix and also compare with missing_values="NaN"
X_zero = np.array([
[1, 0, 1, 1, 1.],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 0],
[6, 6, 0, 6, 6],
])
X_nan = np.array([
[1, np.nan, 1, 1, 1.],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, np.nan],
[6, 6, np.nan, 6, 6],
])
statistics_mean = np.nanmean(X_nan, axis=0)
X_imputed = np.array([
[1, 2.5, 1, 1, 1.],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 1.5],
[6, 6, 2.5, 6, 6],
])
imputer_zero = KNNImputer(missing_values=0,
n_neighbors=2,
weights="uniform")
imputer_nan = KNNImputer(missing_values="NaN",
n_neighbors=2,
weights="uniform")
assert_array_equal(imputer_zero.fit_transform(X_zero), X_imputed)
assert_array_equal(imputer_zero.statistics_, statistics_mean)
assert_array_equal(imputer_zero.fit_transform(X_zero),
imputer_nan.fit_transform(X_nan))
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Author : qichun tang # @Contact : [email protected] from copy import deepcopy import pandas as pd import numpy as np from sklearn.preprocessing import LabelEncoder from collections import Counter df = pd.read_csv("train_classification.csv") df_ce = deepcopy(df) for name in ["Name", "Sex", "Ticket", "Fare", "Cabin", "Embarked"]: col = df_ce[name] col[~col.isna()] = LabelEncoder().fit_transform(col[~col.isna()]) from skimpute import MissForest, KNNImputer imputer = KNNImputer() imp = imputer.fit_transform(df_ce.values.astype("float")) imp_df = pd.DataFrame(imp, columns=df_ce.columns) print(Counter(imp_df["Sex"])) print(Counter(imp_df["Cabin"])) print(Counter(imp_df["Embarked"]))
def test_knn_imputation_default():
# Test imputation with default parameter values
# Test with an imputable matrix
X = np.array([
[1, 0, 0, 1],
[2, 1, 2, np.nan],
[3, 2, 3, np.nan],
[np.nan, 4, 5, 5],
[6, np.nan, 6, 7],
[8, 8, 8, 8],
[16, 15, 18, 19],
])
statistics_mean = np.nanmean(X, axis=0)
X_imputed = np.array([
[1, 0, 0, 1],
[2, 1, 2, 8],
[3, 2, 3, 8],
[4, 4, 5, 5],
[6, 3, 6, 7],
[8, 8, 8, 8],
[16, 15, 18, 19],
])
imputer = KNNImputer()
assert_array_equal(imputer.fit_transform(X), X_imputed)
assert_array_equal(imputer.statistics_, statistics_mean)
# Test with % missing in row > row_max_missing
X = np.array([
[1, 0, 0, 1],
[2, 1, 2, np.nan],
[3, 2, 3, np.nan],
[np.nan, 4, 5, 5],
[6, np.nan, 6, 7],
[8, 8, 8, 8],
[19, 19, 19, 19],
[np.nan, np.nan, np.nan, 19],
])
statistics_mean = np.nanmean(X, axis=0)
r7c0, r7c1, r7c2, _ = statistics_mean
X_imputed = np.array([
[1, 0, 0, 1],
[2, 1, 2, 8],
[3, 2, 3, 8],
[4, 4, 5, 5],
[6, 3, 6, 7],
[8, 8, 8, 8],
[19, 19, 19, 19],
[r7c0, r7c1, r7c2, 19],
])
imputer = KNNImputer()
assert_array_almost_equal(imputer.fit_transform(X), X_imputed, decimal=6)
assert_array_almost_equal(imputer.statistics_, statistics_mean, decimal=6)
# Test with all neighboring donors also having missing feature values
X = np.array([[1, 0, 0, np.nan], [2, 1, 2, np.nan], [3, 2, 3, np.nan],
[4, 4, 5, np.nan], [6, 7, 6, np.nan], [8, 8, 8, np.nan],
[20, 20, 20, 20], [22, 22, 22, 22]])
statistics_mean = np.nanmean(X, axis=0)
X_imputed = np.array([[1, 0, 0, 21], [2, 1, 2, 21], [3, 2, 3, 21],
[4, 4, 5, 21], [6, 7, 6, 21], [8, 8, 8, 21],
[20, 20, 20, 20], [22, 22, 22, 22]])
imputer = KNNImputer()
assert_array_equal(imputer.fit_transform(X), X_imputed)
assert_array_equal(imputer.statistics_, statistics_mean)
# Test when data in fit() and transform() are different
X = np.array([[0, 0], [np.nan, 2], [4, 3], [5, 6], [7, 7], [9, 8],
[11, 16]])
statistics_mean = np.nanmean(X, axis=0)
Y = np.array([[1, 0], [3, 2], [4, np.nan]])
Y_imputed = np.array([[1, 0], [3, 2], [4, 4.8]])
imputer = KNNImputer()
assert_array_equal(imputer.fit(X).transform(Y), Y_imputed)
assert_array_equal(imputer.statistics_, statistics_mean)
def test_weight_distance():
X = np.array([[0, 0], [np.nan, 2], [4, 3], [5, 6], [7, 7], [9, 8],
[11, 10]])
# Test with "distance" weight
# Get distance of "n_neighbors" neighbors of row 1
dist_matrix = pairwise_distances(X, metric="masked_euclidean")
index = np.argsort(dist_matrix)[1, 1:6]
dist = dist_matrix[1, index]
weights = 1 / dist
values = X[index, 0]
imputed = np.dot(values, weights) / np.sum(weights)
# Manual calculation
X_imputed_distance1 = np.array([[0, 0], [3.850394, 2], [4, 3], [5, 6],
[7, 7], [9, 8], [11, 10]])
# NearestNeighbor calculation
X_imputed_distance2 = np.array([[0, 0], [imputed, 2], [4, 3], [5, 6],
[7, 7], [9, 8], [11, 10]])
imputer = KNNImputer(weights="distance")
assert_array_almost_equal(imputer.fit_transform(X),
X_imputed_distance1,
decimal=6)
assert_array_almost_equal(imputer.fit_transform(X),
X_imputed_distance2,
decimal=6)
# Test with weights = "distance" and n_neighbors=2
X = np.array([
[np.nan, 0, 0],
[2, 1, 2],
[3, 2, 3],
[4, 5, 5],
])
statistics_mean = np.nanmean(X, axis=0)
X_imputed = np.array([
[2.3828, 0, 0],
[2, 1, 2],
[3, 2, 3],
[4, 5, 5],
])
imputer = KNNImputer(n_neighbors=2, weights="distance")
assert_array_almost_equal(imputer.fit_transform(X), X_imputed, decimal=4)
assert_array_equal(imputer.statistics_, statistics_mean)
# Test with varying missingness patterns
X = np.array([
[1, 0, 0, 1],
[0, np.nan, 1, np.nan],
[1, 1, 1, np.nan],
[0, 1, 0, 0],
[0, 0, 0, 0],
[1, 0, 1, 1],
[10, 10, 10, 10],
])
statistics_mean = np.nanmean(X, axis=0)
# Get weights of donor neighbors
dist = masked_euclidean_distances(X)
r1c1_nbor_dists = dist[1, [0, 2, 3, 4, 5]]
r1c3_nbor_dists = dist[1, [0, 3, 4, 5, 6]]
r1c1_nbor_wt = (1 / r1c1_nbor_dists)
r1c3_nbor_wt = (1 / r1c3_nbor_dists)
r2c3_nbor_dists = dist[2, [0, 3, 4, 5, 6]]
r2c3_nbor_wt = 1 / r2c3_nbor_dists
# Collect donor values
col1_donor_values = np.ma.masked_invalid(X[[0, 2, 3, 4, 5], 1]).copy()
col3_donor_values = np.ma.masked_invalid(X[[0, 3, 4, 5, 6], 3]).copy()
# Final imputed values
r1c1_imp = np.ma.average(col1_donor_values, weights=r1c1_nbor_wt)
r1c3_imp = np.ma.average(col3_donor_values, weights=r1c3_nbor_wt)
r2c3_imp = np.ma.average(col3_donor_values, weights=r2c3_nbor_wt)
print(r1c1_imp, r1c3_imp, r2c3_imp)
X_imputed = np.array([
[1, 0, 0, 1],
[0, r1c1_imp, 1, r1c3_imp],
[1, 1, 1, r2c3_imp],
[0, 1, 0, 0],
[0, 0, 0, 0],
[1, 0, 1, 1],
[10, 10, 10, 10],
])
imputer = KNNImputer(weights="distance")
assert_array_almost_equal(imputer.fit_transform(X), X_imputed, decimal=6)
assert_array_equal(imputer.statistics_, statistics_mean)