Zindi South African Covid-19 vulnerability map
South African COVID-19 Vulnerability Map by #ZindiWeekendz
• 230 min read
import numpy as np
import os
import glob
import pandas as pd
from google.colab import drive
import cv2
from pathlib import Path
import xgboost as xgb
drive.mount('/content/drive')
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly Enter your authorization code: ·········· Mounted at /content/drive
data=pd.read_csv('/content/drive/My Drive/Zindi_Hack/Train_maskedv2.csv')
print('The dimensions of training data is:',data.shape)
print('------------------------------------------------')
print(data.head(5))
The dimensions of training data is: (3174, 50)
------------------------------------------------
ward total_households total_individuals ... pg_03 pg_04 lgt_00
0 9D9BEUB 13569.97801 39024.03083 ... 0.029031 0.010292 0.599259
1 RERH3XM 13593.88256 32879.94646 ... 0.000586 0.002832 0.699136
2 GJWA3BO 2698.30050 8261.71093 ... 0.003201 0.000663 0.972315
3 GW6VRB7 1965.96698 9069.77694 ... 0.054701 0.002299 0.735333
4 CN9JZJC 1852.07821 9908.84404 ... 0.000704 0.000714 0.646964
[5 rows x 50 columns]
#Let us try and find out the missing values in the train data
data.isna().sum()
# Looks like no missing data
ward 0 total_households 0 total_individuals 0 target_pct_vunerable 0 dw_00 0 dw_01 0 dw_02 0 dw_03 0 dw_04 0 dw_05 0 dw_06 0 dw_07 0 dw_08 0 dw_09 0 dw_10 0 dw_11 0 dw_12 0 dw_13 0 psa_00 0 psa_01 0 psa_02 0 psa_03 0 psa_04 0 stv_00 0 stv_01 0 car_00 0 car_01 0 lln_00 0 lln_01 0 lan_00 0 lan_01 0 lan_02 0 lan_03 0 lan_04 0 lan_05 0 lan_06 0 lan_07 0 lan_08 0 lan_09 0 lan_10 0 lan_11 0 lan_12 0 lan_13 0 lan_14 0 pg_00 0 pg_01 0 pg_02 0 pg_03 0 pg_04 0 lgt_00 0 dtype: int64
# Let us try and find out if there any non-numeric data in the training data
non_numeric=[cols for cols in data.columns if data[cols].dtypes=='object']
non_numeric
# Obviously ward is an alpha numeric value used for uniquely determining the locality
['ward']
import matplotlib.pyplot as plt
%matplotlib inline
f = plt.figure(figsize=(19, 15))
plt.matshow(data.corr(), fignum=f.number)
plt.xticks(range(data.shape[1]), data.columns, fontsize=14, rotation=45)
plt.yticks(range(data.shape[1]), data.columns, fontsize=14)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=14)
plt.title('Correlation Matrix', fontsize=16);
plt.show()
data.columns
Index(['ward', 'total_households', 'total_individuals', 'target_pct_vunerable',
'dw_00', 'dw_01', 'dw_02', 'dw_03', 'dw_04', 'dw_05', 'dw_06', 'dw_07',
'dw_08', 'dw_09', 'dw_10', 'dw_11', 'dw_12', 'dw_13', 'psa_00',
'psa_01', 'psa_02', 'psa_03', 'psa_04', 'stv_00', 'stv_01', 'car_00',
'car_01', 'lln_00', 'lln_01', 'lan_00', 'lan_01', 'lan_02', 'lan_03',
'lan_04', 'lan_05', 'lan_06', 'lan_07', 'lan_08', 'lan_09', 'lan_10',
'lan_11', 'lan_12', 'lan_13', 'lan_14', 'pg_00', 'pg_01', 'pg_02',
'pg_03', 'pg_04', 'lgt_00'],
dtype='object')
#Lets split the data
from sklearn.model_selection import train_test_split
train_cols= ['total_households', 'total_individuals',
'dw_00', 'dw_01', 'dw_02', 'dw_03', 'dw_04', 'dw_05', 'dw_06', 'dw_07',
'dw_08', 'dw_09', 'dw_10', 'dw_11', 'dw_12', 'dw_13', 'psa_00',
'psa_01', 'psa_02', 'psa_03', 'psa_04', 'stv_00', 'stv_01', 'car_00',
'car_01', 'lln_00', 'lln_01', 'lan_00', 'lan_01', 'lan_02', 'lan_03',
'lan_04', 'lan_05', 'lan_06', 'lan_07', 'lan_08', 'lan_09', 'lan_10',
'lan_11', 'lan_12', 'lan_13', 'lan_14', 'pg_00', 'pg_01', 'pg_02',
'pg_03', 'pg_04', 'lgt_00']
target_cols=['target_pct_vunerable']
X=data[train_cols]
y=data[target_cols]
train_X, val_X, train_y, val_y = train_test_split(X, y,test_size=0.50,random_state = 123)
print('The training data dimensions are:',train_X.shape,'------',train_y.shape)
print('The validation data dimensions are:',val_X.shape,'------',val_y.shape)
The training data dimensions are: (1587, 48) ------ (1587, 1) The validation data dimensions are: (1587, 48) ------ (1587, 1)
#Lets try a simple random forest regressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomForestRegressor(random_state=123)
forest_model.fit(train_X, train_y)
preds = forest_model.predict(val_X)
print(mean_absolute_error(val_y, preds))
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). after removing the cwd from sys.path.
3.8639445471437255
#Lets see the variable importance
importances = forest_model.feature_importances_
indices = np.argsort(importances)[:10]
plt.title('Top 10 Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [train_cols[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()
#Lets try and find out the cross-validation accuracy of a random forest model
from sklearn.model_selection import cross_val_score
scores = cross_val_score(forest_model, X, y, cv=5,scoring='neg_mean_absolute_error')
print("Mean absolute error : %0.2f (+/- %0.2f)" % (-scores.mean(), scores.std() * 2))
/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). estimator.fit(X_train, y_train, **fit_params) /usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). estimator.fit(X_train, y_train, **fit_params) /usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). estimator.fit(X_train, y_train, **fit_params) /usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). estimator.fit(X_train, y_train, **fit_params) /usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). estimator.fit(X_train, y_train, **fit_params)
Mean absolute error : 3.93 (+/- 0.26)
#Now lets try xgboost
import xgboost as xgb
xgb_reg=xgb.XGBRegressor(random_state=123)
xgb_reg.fit(train_X,train_y)
#print('Now cross validating')
#print('-------------------------------')
#xgb_scores = cross_val_score(xgb_reg, X, y, cv=5,scoring='neg_mean_absolute_error')
#print("Mean absolute error : %0.2f (+/- %0.2f)" % (-xgb_scores.mean(), xgb_scores.std() * 2))
[19:30:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0,
importance_type='gain', learning_rate=0.1, max_delta_step=0,
max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
n_jobs=1, nthread=None, objective='reg:linear', random_state=123,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=1, verbosity=1)
preds = xgb_reg.predict(val_X)
print(mean_absolute_error(val_y, preds))
4.045821563802859
X['new']=xgb_reg.predict(X)
X.shape
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy """Entry point for launching an IPython kernel.
(3174, 49)
importances = xgb_reg.feature_importances_
indices = np.argsort(importances)[:10]
plt.title('Top 10 Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [train_cols[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()
!pip install catboost
Collecting catboost
Downloading https://files.pythonhosted.org/packages/94/ec/12b9a42b2ea7dfe5b602f235692ab2b61ee1334ff34334a15902272869e8/catboost-0.22-cp36-none-manylinux1_x86_64.whl (64.4MB)
|████████████████████████████████| 64.4MB 61kB/s
Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)
Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)
Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)
Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.0.3)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.12.0)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.1)
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Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.1.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.6)
Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (46.0.0)
Installing collected packages: catboost
Successfully installed catboost-0.22
#Now lets try CatBoost
from catboost import CatBoostRegressor
from sklearn.model_selection import KFold
kf = KFold(n_splits=5)
catboost_scores=0.0
fold = 0
catboost_reg=CatBoostRegressor(random_state=123)
for train_index, test_index in kf.split(X):
print('In fold',fold+1)
X_train, X_test = X.iloc[train_index][:], X.iloc[test_index][:]
y_train, y_test = y.iloc[train_index][:], y.iloc[test_index][:]
catboost_reg.fit(X_train,y_train,use_best_model=True,eval_set=(X_test,y_test),plot=True,early_stopping_rounds=3)
catboost_scores=catboost_scores+mean_absolute_error(y_test,catboost_reg.predict(X_test))
fold = fold + 1
print('The cross validation MAE:',catboost_scores/5)
In fold 1
Learning rate set to 0.05495 0: learn: 12.3289345 test: 12.2776040 best: 12.2776040 (0) total: 19.4ms remaining: 19.4s 1: learn: 11.8446509 test: 11.7949884 best: 11.7949884 (1) total: 38.8ms remaining: 19.3s 2: learn: 11.3818081 test: 11.3412546 best: 11.3412546 (2) total: 75.9ms remaining: 25.2s 3: learn: 10.9612010 test: 10.9279565 best: 10.9279565 (3) total: 101ms remaining: 25.1s 4: learn: 10.5766760 test: 10.5443191 best: 10.5443191 (4) total: 121ms remaining: 24.1s 5: learn: 10.2119507 test: 10.2035258 best: 10.2035258 (5) total: 150ms remaining: 24.8s 6: learn: 9.8588449 test: 9.8582769 best: 9.8582769 (6) total: 169ms remaining: 24s 7: learn: 9.5429266 test: 9.5386999 best: 9.5386999 (7) total: 183ms remaining: 22.7s 8: learn: 9.2369364 test: 9.2339978 best: 9.2339978 (8) total: 200ms remaining: 22s 9: learn: 8.9508855 test: 8.9604158 best: 8.9604158 (9) total: 213ms remaining: 21.1s 10: learn: 8.6719697 test: 8.6852428 best: 8.6852428 (10) total: 226ms remaining: 20.3s 11: 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In fold 2
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In fold 3
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In fold 4
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The cross validation MAE: 3.5282094215572797
grid = {'learning_rate': [0.01,0.03, 0.1],
'depth': [4, 6, 15],
'l2_leaf_reg': [1, 3, 5, 7, 9]}
randomized_search_result = catboost_reg.randomized_search(grid,
X=X,
y=y,
cv=5
)
print('Now cross validating')
print('-------------------------------')
catboost_scores = cross_val_score(catboost_reg, X, y, cv=5,scoring='neg_mean_absolute_error')
print("Mean absolute error : %0.2f (+/- %0.2f)" % (-catboost_scores.mean(), catboost_scores.std() * 2))
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Mean absolute error : 3.21 (+/- 0.23)
#Lets try a LightGBM model
import lightgbm as lgbm
lgbm_reg=lgbm.LGBMRegressor(random_state=123)
kf = KFold(n_splits=5)
lgbm_scores=0.0
fold = 0
for train_index, test_index in kf.split(X):
print('In fold',fold+1)
X_train, X_test = X.iloc[train_index][:], X.iloc[test_index][:]
y_train, y_test = y.iloc[train_index][:], y.iloc[test_index][:]
lgbm_reg.fit(X_train,y_train,eval_set=(X_test,y_test),early_stopping_rounds=3,metric=)
lgbm_scores=lgbm_scores+mean_absolute_error(y_test,lgbm_reg.predict(X_test))
fold = fold + 1
print('The cross validation MAE:',lgbm_scores/5)
test=pd.read_csv('/content/drive/My Drive/Zindi_Hack/Test_maskedv2.csv')
print('The dimension of test data:',test.shape)
The dimension of test data: (1102, 49)
test.head()
| ward | total_households | total_individuals | dw_00 | dw_01 | dw_02 | dw_03 | dw_04 | dw_05 | dw_06 | dw_07 | dw_08 | dw_09 | dw_10 | dw_11 | dw_12 | dw_13 | psa_00 | psa_01 | psa_02 | psa_03 | psa_04 | stv_00 | stv_01 | car_00 | car_01 | lln_00 | lln_01 | lan_00 | lan_01 | lan_02 | lan_03 | lan_04 | lan_05 | lan_06 | lan_07 | lan_08 | lan_09 | lan_10 | lan_11 | lan_12 | lan_13 | lan_14 | pg_00 | pg_01 | pg_02 | pg_03 | pg_04 | lgt_00 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 00A93O0 | 2514.04172 | 8037.06199 | 0.671962 | 0.061074 | 0.101516 | 0.006891 | 0.021287 | 0.015615 | 0.035974 | 0.010995 | 0.030665 | 0.000563 | 0.004513 | 0.038943 | 0 | 0 | 0.327785 | 0.400204 | 0.000142 | 0.090634 | 0.181234 | 0.082473 | 0.917527 | 0.170320 | 0.829680 | 0.028193 | 0.971807 | 0.006861 | 0.025960 | 0.001401 | 0.864523 | 0.010393 | 0.001966 | 0.004094 | 0.000991 | 0.007919 | 0.000136 | 0.000146 | 0.000420 | 0.011485 | 0 | 0.063705 | 0.979272 | 0.009926 | 0.003224 | 0.002697 | 0.004882 | 0.521007 |
| 1 | 01V40KO | 1829.71944 | 7494.86917 | 0.542857 | 0.390850 | 0.039623 | 0.000000 | 0.000000 | 0.000000 | 0.002633 | 0.005074 | 0.010439 | 0.000000 | 0.000544 | 0.007980 | 0 | 0 | 0.396451 | 0.448673 | 0.002026 | 0.015543 | 0.137307 | 0.028539 | 0.971461 | 0.087096 | 0.912904 | 0.014139 | 0.985861 | 0.003348 | 0.008866 | 0.001654 | 0.968007 | 0.005108 | 0.002472 | 0.001524 | 0.001974 | 0.006575 | 0.000152 | 0.000160 | 0.000000 | 0.000160 | 0 | 0.000000 | 0.998938 | 0.000296 | 0.000301 | 0.000305 | 0.000160 | 0.693716 |
| 2 | 01ZHVW0 | 1721.99009 | 5847.43254 | 0.968071 | 0.003525 | 0.001431 | 0.000000 | 0.000000 | 0.000000 | 0.018546 | 0.004769 | 0.000825 | 0.000000 | 0.000000 | 0.002832 | 0 | 0 | 0.315332 | 0.550687 | 0.000000 | 0.009654 | 0.124326 | 0.108260 | 0.891740 | 0.115144 | 0.884856 | 0.047064 | 0.952936 | 0.037169 | 0.019334 | 0.001471 | 0.918999 | 0.003145 | 0.001087 | 0.001963 | 0.001543 | 0.005908 | 0.000000 | 0.002075 | 0.000631 | 0.006504 | 0 | 0.000171 | 0.955895 | 0.012170 | 0.000000 | 0.030012 | 0.001923 | 0.933954 |
| 3 | 028YQJX | 12388.02257 | 52211.66859 | 0.397274 | 0.004734 | 0.001068 | 0.000279 | 0.000799 | 0.197760 | 0.005227 | 0.065487 | 0.316633 | 0.003892 | 0.000279 | 0.006569 | 0 | 0 | 0.242343 | 0.568208 | 0.001050 | 0.066225 | 0.122174 | 0.146214 | 0.853786 | 0.223657 | 0.776343 | 0.174330 | 0.825670 | 0.485175 | 0.240164 | 0.002869 | 0.242053 | 0.003676 | 0.000726 | 0.008142 | 0.003545 | 0.003591 | 0.000629 | 0.000505 | 0.000593 | 0.008330 | 0 | 0.000000 | 0.293629 | 0.694779 | 0.003238 | 0.000628 | 0.007726 | 0.921470 |
| 4 | 02G8W11 | 1754.25716 | 4266.57609 | 0.815197 | 0.016697 | 0.078571 | 0.008975 | 0.047882 | 0.015398 | 0.007264 | 0.000000 | 0.000712 | 0.007003 | 0.000877 | 0.001425 | 0 | 0 | 0.110625 | 0.663775 | 0.000000 | 0.024673 | 0.200927 | 0.723028 | 0.276972 | 0.930886 | 0.069114 | 0.808671 | 0.191329 | 0.457734 | 0.334764 | 0.000843 | 0.005681 | 0.001384 | 0.000000 | 0.002514 | 0.000829 | 0.000284 | 0.000290 | 0.000000 | 0.000234 | 0.016846 | 0 | 0.178598 | 0.110861 | 0.054114 | 0.006062 | 0.823495 | 0.005470 | 0.996227 |
test['new']=xgb_reg.predict(test[train_cols])
test.shape
(1102, 50)
sample=pd.read_csv('/content/drive/My Drive/Zindi_Hack/samplesubmissionv2.csv')
sample.head()
| ward | target_pct_vunerable | |
|---|---|---|
| 0 | 00A93O0 | NaN |
| 1 | 01V40KO | NaN |
| 2 | 01ZHVW0 | NaN |
| 3 | 028YQJX | NaN |
| 4 | 02G8W11 | NaN |
sample['target_pct_vunerable']=(catboost_reg.predict(test[train_cols+['new']]))
sample.shape
(1102, 2)
sample.head()
| ward | target_pct_vunerable | |
|---|---|---|
| 0 | 00A93O0 | 17.669271 |
| 1 | 01V40KO | 35.082365 |
| 2 | 01ZHVW0 | 1.799878 |
| 3 | 028YQJX | 2.715354 |
| 4 | 02G8W11 | 0.772809 |
sample.to_csv('/content/drive/My Drive/Zindi_Hack/submission_stack.csv',index=False)