Sorsogon. Step 1.d Micro-level Water demand model¶
In [1]:
import datetime; print(datetime.datetime.now())
2018-03-15 13:52:14.007748
Notebook abstract
A simple micro-level water demand model.
A simple micro-level water demand model. Similar to the income demand model and the electricity demand model, the water demand model uses micro level consumption demand data for the construction of a table model. The table model describes simple rules for the construction of the proxy micro level sample data.
Prior water demand model¶
In [2]:
import statsmodels.api as sm
import pandas as pd
import numpy as np
from smum._scripts.micro import compute_categories, change_index
/usr/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
from pandas.core import datetools
In [3]:
water_data = pd.read_csv('data/water.csv', index_col=0)
formula = "Water_expenditure ~ Total_Family_Income + Family_Size + C(HH_head_Sex)\
+ HH_head_Age + C(Education) + C(Urbanity)"
In [4]:
water_data.head()
Out[4]:
| Family_Size | HH_head_Sex | HH_head_Age | Education | Electricity_expenditure | Water_expenditure | Total_Family_Income | Urbanity | |
|---|---|---|---|---|---|---|---|---|
| 3 | 2.0 | 2 | 51 | 1.0 | 900 | 2190 | 9932.333333 | 0 |
| 9 | 3.5 | 1 | 41 | 4.0 | 17202 | 300 | 47233.833333 | 0 |
| 11 | 3.0 | 2 | 75 | 1.0 | 4212 | 3024 | 16521.333333 | 0 |
| 12 | 4.5 | 1 | 74 | 1.0 | 6210 | 4086 | 20254.333333 | 0 |
| 13 | 6.5 | 2 | 55 | 2.0 | 3900 | 2940 | 16368.000000 | 0 |
In [5]:
model_water = sm.WLS.from_formula(formula, water_data)
model_results_water = model_water.fit()
In [6]:
model_results_water.summary()
Out[6]:
| Dep. Variable: | Water_expenditure | R-squared: | 0.344 |
|---|---|---|---|
| Model: | WLS | Adj. R-squared: | 0.344 |
| Method: | Least Squares | F-statistic: | 925.6 |
| Date: | Thu, 15 Mar 2018 | Prob (F-statistic): | 0.00 |
| Time: | 13:52:16 | Log-Likelihood: | -1.3853e+05 |
| No. Observations: | 15904 | AIC: | 2.771e+05 |
| Df Residuals: | 15894 | BIC: | 2.771e+05 |
| Df Model: | 9 | ||
| Covariance Type: | nonrobust |
| coef | std err | t | P>|t| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Intercept | -601.5920 | 62.632 | -9.605 | 0.000 | -724.358 | -478.826 |
| C(HH_head_Sex)[T.2] | 98.4950 | 29.444 | 3.345 | 0.001 | 40.782 | 156.208 |
| C(Education)[T.2.0] | 214.4011 | 28.816 | 7.440 | 0.000 | 157.919 | 270.883 |
| C(Education)[T.3.0] | 260.3273 | 40.057 | 6.499 | 0.000 | 181.810 | 338.844 |
| C(Education)[T.4.0] | 101.7028 | 49.996 | 2.034 | 0.042 | 3.705 | 199.700 |
| C(Education)[T.5.0] | 40.1879 | 119.681 | 0.336 | 0.737 | -194.400 | 274.775 |
| C(Urbanity)[T.1] | 1000.9789 | 25.416 | 39.384 | 0.000 | 951.161 | 1050.797 |
| Total_Family_Income | 0.0532 | 0.001 | 54.145 | 0.000 | 0.051 | 0.055 |
| Family_Size | 49.7394 | 5.898 | 8.434 | 0.000 | 38.179 | 61.300 |
| HH_head_Age | 6.0889 | 0.913 | 6.671 | 0.000 | 4.300 | 7.878 |
| Omnibus: | 3226.790 | Durbin-Watson: | 1.399 |
|---|---|---|---|
| Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 7597.976 |
| Skew: | 1.142 | Prob(JB): | 0.00 |
| Kurtosis: | 5.499 | Cond. No. | 3.10e+05 |
In [7]:
params_water = change_index(model_results_water.params)
bse_water = change_index(model_results_water.bse)
water = pd.concat([params_water, bse_water], axis=1)
water.columns = ['co_mu', 'co_sd']
water = compute_categories(water)
In [8]:
water.loc['Urbanity', 'p'] = (water_data.Urbanity == 1).sum() / water_data.shape[0]
water.loc['Sex', 'p'] = (water_data.HH_head_Sex == 2).sum() / water_data.shape[0]
In [9]:
water.loc[:,'dis'] = 'None'
water.loc['Education', 'dis'] = 'None;i;Categorical'
water.loc['Intercept', 'dis'] = 'Deterministic'
In [10]:
water.loc[:, 'mu'] = np.nan
water.loc[:, 'sd'] = np.nan
water.loc['Intercept', 'p'] = water.loc['Intercept', 'co_mu']
water.loc['Intercept', ['co_mu', 'co_sd']] = np.nan
In [11]:
water.loc[:,'ub'] = np.nan
water.loc[:,'lb'] = np.nan
In [12]:
water.index = ['w_'+i for i in water.index]
In [13]:
water.to_csv('data/table_water.csv')
In [14]:
water
Out[14]:
| co_mu | co_sd | p | dis | mu | sd | ub | lb | |
|---|---|---|---|---|---|---|---|---|
| w_Intercept | NaN | NaN | -601.591950 | Deterministic | NaN | NaN | NaN | NaN |
| w_Sex | 98.495 | 29.4438 | 0.224786 | None | NaN | NaN | NaN | NaN |
| w_Urbanity | 1000.98 | 25.4159 | 0.593939 | None | NaN | NaN | NaN | NaN |
| w_Total_Family_Income | 0.053187 | 0.000982306 | NaN | None | NaN | NaN | NaN | NaN |
| w_FamilySize | 49.7394 | 5.89779 | NaN | None | NaN | NaN | NaN | NaN |
| w_Age | 6.08894 | 0.912741 | NaN | None | NaN | NaN | NaN | NaN |
| w_Education | 1.0,214.4011453125436,260.32727427717964,101.7... | 1e-10,28.815802440470176,40.0574490885231,49.9... | NaN | None;i;Categorical | NaN | NaN | NaN | NaN |