From 7fd1f822e27530ffa5304415694c5dbfa40a1f87 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 23 Oct 2023 19:14:08 -0400 Subject: [PATCH 01/11] remove surv_rate, make rho 2d --- ogmys/ogmys_default_parameters.json | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/ogmys/ogmys_default_parameters.json b/ogmys/ogmys_default_parameters.json index 47659b8..a51fa2e 100644 --- a/ogmys/ogmys_default_parameters.json +++ b/ogmys/ogmys_default_parameters.json @@ -141,8 +141,8 @@ "rho_G": 0.1, "debt_ratio_ss": 1.0, "initial_debt_ratio": 0.62, - "r_gov_scale": 1.0, - "r_gov_shift": 0.00, + "r_gov_scale": [1.0], + "r_gov_shift": [0.00], "M": 2, "I": 2, "io_matrix": [ @@ -1736,8 +1736,7 @@ "imm_rates": [ [0.0062112432351020715, 0.006695391269304812, 0.0072416059873147915, 0.007590577071078806, 0.007646147746699469, 0.00751951109796381, 0.007066814891074261, 0.006564297706210947, 0.006073197537540996, 0.005507000333471546, 0.004947327413337241, 0.004549582594943434, 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4.2967418446471474e-12, 7.782683835668153e-06, 0.30558240211849436, 0.05926264715994571, 0.9980598728013174, 0.053064066767033215, 0.053064066767033215, 0.002525183353514611, 6.198580392912497e-05, 0.053064066767033215], [1.5414821784144602e-11, 1.5030110129145822e-05, 4.2967418446471474e-12, 7.782683835668153e-06, 0.30558240211849436, 0.05926264715994571, 0.9980598728013174, 0.053064066767033215, 0.053064066767033215, 0.002525183353514611, 6.198580392912497e-05, 0.053064066767033215], [1.5414821784144602e-11, 1.5030110129145822e-05, 4.2967418446471474e-12, 7.782683835668153e-06, 0.30558240211849436, 0.05926264715994571, 0.9980598728013174, 0.053064066767033215, 0.053064066767033215, 0.002525183353514611, 6.198580392912497e-05, 0.053064066767033215]] ], From 9a25838a80d508a721e84a33448cad6660dc1e68 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Mon, 23 Oct 2023 20:22:39 -0400 Subject: [PATCH 02/11] remove refs to surv rate in demog comments --- ogmys/demographics.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/ogmys/demographics.py b/ogmys/demographics.py index 1edb7df..75c8d6d 100755 --- a/ogmys/demographics.py +++ b/ogmys/demographics.py @@ -415,8 +415,6 @@ def get_pop_objs( g_n_SS (scalar): steady-state population growth rate omega_SS (Numpy array): normalized steady-state population distribution, length S - surv_rates (Numpy array): survival rates that correspond to - each model period of life, length S mort_rates (Numpy array): mortality rates that correspond to each model period of life, length S g_n_path (Numpy array): population growth rates over the time @@ -675,7 +673,7 @@ def get_pop_objs( output_dir=OUTPUT_DIR, ) - # return omega_path_S, g_n_SS, omega_SSfx, survival rates, + # return omega_path_S, g_n_SS, omega_SSfx, # mort_rates_S, and g_n_path pop_dict = { "omega": omega_path_S.T, From 80e3f100f9d3d3116586bbba80483d75bb2d0152 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Thu, 30 Nov 2023 12:39:57 -0500 Subject: [PATCH 03/11] remove surv_rate --- examples/run_og_mys.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/run_og_mys.py b/examples/run_og_mys.py index d10f0a7..d5a2781 100644 --- a/examples/run_og_mys.py +++ b/examples/run_og_mys.py @@ -56,7 +56,6 @@ def main(): "omega": d["omega"], "g_n_ss": d["g_n_ss"], "omega_SS": d["omega_SS"], - "surv_rate": d["surv_rate"], "rho": d["rho"], "g_n": d["g_n"], "imm_rates": d["imm_rates"], From 2e111eef5d21df93509c24304961f9f0576cf5ac Mon Sep 17 00:00:00 2001 From: jdebacker Date: Thu, 30 Nov 2023 12:40:25 -0500 Subject: [PATCH 04/11] update funtion to fit e mat --- ogmys/calibrate.py | 12 +- ogmys/income.py | 473 +++++++++------------------------------------ 2 files changed, 99 insertions(+), 386 deletions(-) diff --git a/ogmys/calibrate.py b/ogmys/calibrate.py index 5fce029..867644c 100644 --- a/ogmys/calibrate.py +++ b/ogmys/calibrate.py @@ -62,17 +62,17 @@ def __init__( p.start_year, GraphDiag=True, ) - # demographics for 80 period lives (needed for getting e below) - demog80 = demographics.get_pop_objs( - 20, 80, p.T, 1, 100, p.start_year - 1, p.start_year - ) # earnings profiles + # Find the e matrix for MYS by changing the earnings profiles for + # the USA to match the gini in MYS + # (=41.1 in WB data: https://data.worldbank.org/indicator/SI.POV.GINI)) self.e = income.get_e_interp( + p.E, p.S, - self.demographic_params["omega_SS"], - demog80["omega_SS"], + p.J, p.lambdas, + self.demographic_params["omega_SS"], plot=False, ) diff --git a/ogmys/income.py b/ogmys/income.py index 5194f4b..c75df9c 100644 --- a/ogmys/income.py +++ b/ogmys/income.py @@ -1,10 +1,3 @@ -""" ------------------------------------------------------------------ -Functions for created the matrix of ability levels, e. This can -only be used for looking at the 25, 50, 70, 80, 90, 99, and 100th -percentiles, as it uses fitted polynomials to those percentiles. ------------------------------------------------------------------ -""" import numpy as np import scipy.optimize as opt import scipy.interpolate as si @@ -19,171 +12,32 @@ OUTPUT_DIR = os.path.join(CUR_PATH, "OUTPUT", "ability") -def arctan_func(xvals, a, b, c): - r""" - This function generates predicted ability levels given data (xvals) - and parameters a, b, and c, from the following arctan function: - - .. math:: - y = (-a / \pi) * \arctan(b * x + c) + (a / 2) - - Args: - xvals (Numpy array): data inputs to arctan function - a (scalar): scale parameter for arctan function - b (scalar): curvature parameter for arctan function - c (scalar): shift parameter for arctan function - - Returns: - yvals (Numpy array): predicted values (output) of arctan - function - +def get_e_interp(E, S, J, lambdas, age_wgts, gini_to_match=41.1, plot=False): """ - yvals = (-a / np.pi) * np.arctan(b * xvals + c) + (a / 2) - return yvals - - -def arctan_deriv_func(xvals, a, b, c): - r""" - This function generates predicted derivatives of arctan function - given data (xvals) and parameters a, b, and c. The functional form - of the derivative of the function is the following: - - .. math:: - y = - (a * b) / (\pi * (1 + (b * xvals + c)^2)) - - Args: - xvals (Numpy array): data inputs to arctan derivative function - a (scalar): scale parameter for arctan function - b (scalar): curvature parameter for arctan function - c (scalar): shift parameter for arctan function - - Returns: - yvals (Numpy array): predicted values (output) of arctan - derivative function - - """ - yvals = -(a * b) / (np.pi * (1 + (b * xvals + c) ** 2)) - return yvals - - -def arc_error(abc_vals, params): - """ - This function returns a vector of errors in the three criteria on - which the arctan function is fit to predict extrapolated ability in - ages 81 to 100.:: - - 1) The arctan function value at age 80 must match the estimated - original function value at age 80. - 2) The arctan function slope at age 80 must match the estimated - original function slope at age 80. - 3) The level of ability at age 100 must be a given fraction - (abil_deprec) below the ability level at age 80. - - Args: - abc_vals (tuple): contains (a,b,c) - - * a (scalar): scale parameter for arctan function - * b (scalar): curvature parameter for arctan function - * c (scalar): shift parameter for arctan function - params (tuple): contains (first_point, coef1, coef2, coef3, - abil_deprec) - - * first_point (scalar): ability level at age 80, > 0 - * coef1 (scalar): coefficient in log ability equation on - linear term in age - * coef2 (scalar): coefficient in log ability equation on - quadratic term in age - * coef3 (scalar): coefficient in log ability equation on - cubic term in age - * abil_deprec (scalar): ability depreciation rate between - ages 80 and 100, in (0, 1). - - Returns: - error_vec (Numpy array): errors ([error1, error2, error3]) - - * error1 (scalar): error between ability level at age 80 - from original function minus the predicted ability at - age 80 from the arctan function given a, b, and c - * error2 (scalar): error between the slope of the original - function at age 80 minus the slope of the arctan - function at age 80 given a, b, and c - * error3 (scalar): error between the ability level at age - 100 predicted by the original model value times - abil_deprec minus the ability predicted by the arctan - function at age 100 given a, b, and c - - """ - a, b, c = abc_vals - first_point, coef1, coef2, coef3, abil_deprec = params - error1 = first_point - arctan_func(80, a, b, c) - if (3 * coef3 * 80**2 + 2 * coef2 * 80 + coef1) < 0: - error2 = ( - 3 * coef3 * 80**2 + 2 * coef2 * 80 + coef1 - ) * first_point - arctan_deriv_func(80, a, b, c) - else: - error2 = -0.02 * first_point - arctan_deriv_func(80, a, b, c) - error3 = abil_deprec * first_point - arctan_func(100, a, b, c) - error_vec = np.array([error1, error2, error3]) - - return error_vec - - -def arctan_fit(first_point, coef1, coef2, coef3, abil_deprec, init_guesses): - """ - This function fits an arctan function to the last 20 years of the - ability levels of a particular ability group to extrapolate - abilities by trying to match the slope in the 80th year and the - ability depreciation rate between years 80 and 100. - - Args: - first_point (scalar): ability level at age 80, > 0 - coef1 (scalar): coefficient in log ability equation on linear - term in age - coef2 (scalar): coefficient in log ability equation on - quadratic term in age - coef3 (scalar): coefficient in log ability equation on cubic - term in age - abil_deprec (scalar): ability depreciation rate between - ages 80 and 100, in (0, 1) - init_guesses (Numpy array): initial guesses - - Returns: - abil_last (Numpy array): extrapolated ability levels for ages - 81 to 100, length 20 - - """ - params = [first_point, coef1, coef2, coef3, abil_deprec] - solution = opt.root(arc_error, init_guesses, args=params, method="lm") - [a, b, c] = solution.x - old_ages = np.linspace(81, 100, 20) - abil_last = arctan_func(old_ages, a, b, c) - return abil_last - - -def get_e_interp(S, age_wgts, age_wgts_80, abil_wgts, plot=False): - """ - This function takes a source matrix of lifetime earnings profiles - (abilities, emat) of size (80, 7), where 80 is the number of ages - and 7 is the number of ability types in the source matrix, and - interpolates new values of a new S x J sized matrix of abilities - using linear interpolation. [NOTE: For this application, cubic - spline interpolation introduces too much curvature.] - - This function also includes the two cases in which J = 9 and J = 10 - that include higher lifetime earning percentiles calibrated using - Piketty and Saez (2003). - + This function takes the calibrated lifetime earnings profiles + (abilities, e matrix) from OG-USA and then adjusts the shape of those + profiles to match the Gini coefficient for another economy. The + Gini coefficient to match is given in the argument gini_to_match. + Note that the calibrated OG-USA e matrix is of size (80, 10), where + 80 is the number of ages and 10 is the number of ability types. + Users of this function specify their own number of age groups (S) + and ability types (J). The function will map the fitted functions + into these dimensions so long as the percentiles of the ability types + given in lambdas is not more refined at the top end than those in + OG-USA (which identifies up to the top 0.1%). Args: + E (int): the age agents become economically active S (int): number of ages to interpolate. This method assumes that - ages are evenly spaced between the beginning of the 21st - year and the end of the 100th year, >= 3 - age_wgts (Numpy array): distribution of population in each age - for the interpolated ages, length S - age_wgts_80 (Numpy array): percent of population in each - one-year age from 21 to 100, length 80 - abil_wgts (Numpy array): distribution of population in each + ages are evenly spaced between the beginning of age E + up to E+S, >= 3 + J (int): number of ability types to interpolate + lambdas (Numpy array): distribution of population in each ability group, length J + age_wgts (Numpy array): distribution of population in each age + group, length S + gini_to_match (float): Gini coefficient to match, default is + 41.1, the Gini coefficient for MYS in 2019 plot (bool): if True, creates plots of emat_orig and the new interpolated emat_new @@ -192,16 +46,9 @@ def get_e_interp(S, age_wgts, age_wgts_80, abil_wgts, plot=False): so that population-weighted average is 1, size SxJ """ - # Get original 80 x 7 ability matrix - abil_wgts_orig = np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01]) - emat_orig = get_e_orig(age_wgts_80, abil_wgts_orig, plot) - - # Now adjust for MYS by changing the e matrix to match the gini in - # MYS (=41.1 in WB data: https://data.worldbank.org/indicator/SI.POV.GINI)) - # Note, USA gini in these data is 41.5 - # pop_dist = abil_wgts.reshape(1, 7) * age_wgts.reshape(80, 1) - gini_mys_data = 41.1 - gini_usa_data = 41.5 + assert lambdas.shape[0] == J + assert age_wgts.shape[0] == S + # Load USA e matrix as a baseline usa_params = Specifications() usa_params.update_specifications( json.load( @@ -210,38 +57,53 @@ def get_e_interp(S, age_wgts, age_wgts_80, abil_wgts, plot=False): ) ) ) - gini_usa_model = utils.Inequality( - emat_orig, usa_params.omega_SS, usa_params.lambdas, 80, 7 - ).gini() + # Define a function that will find the "a" in the equation: + # e_Y = e_USA * exp(a * e_USA) + # such that the e_Y produces a gini coefficient in the model that + # gives the same ratio between the model implied Gini's in the USA + # and the target country and the empirical Gini's in the USA and given + # by gin_to_match for the target country def f( a, emat_orig, age_wgts, abil_wgts, - gini_mys_data, + gini_to_match, gini_usa_data, gini_usa_model, ): - gini_mys_model = utils.Inequality( + gini_target_model = utils.Inequality( emat_orig * np.exp(a * emat_orig), age_wgts, abil_wgts, - 80, - 7, + len(age_wgts), + len(abil_wgts), ).gini() - error = (gini_mys_data / gini_usa_data) - ( - gini_mys_model / gini_usa_model + error = (gini_to_match / gini_usa_data) - ( + gini_target_model / gini_usa_model ) return error + # Note, USA gini in the World Bank data is 41.5 + # See https://data.worldbank.org/indicator/SI.POV.GINI + gini_usa_data = 41.5 + # Find the model implied Gini for the USA + gini_usa_model = utils.Inequality( + usa_params.e, + usa_params.omega_SS, + usa_params.lambdas, + usa_params.S, + usa_params.J, + ).gini() + x = opt.root_scalar( f, args=( - emat_orig, - age_wgts, - abil_wgts, - gini_mys_data, + usa_params.e, + usa_params.omega_SS, + usa_params.lambdas, + gini_to_match, gini_usa_data, gini_usa_model, ), @@ -250,65 +112,77 @@ def f( xtol=1e-10, ) a = x.root - e_new = emat_orig * np.exp(a * emat_orig) - e_new_scaled = ( + e_new = usa_params.e * np.exp(a * usa_params.e) + emat_new_scaled = ( e_new - / (e_new * age_wgts.reshape(80, 1) * abil_wgts.reshape(1, 7)).sum() + / ( + e_new + * usa_params.omega_SS.reshape(usa_params.S, 1) + * usa_params.lambdas.reshape(1, usa_params.J) + ).sum() ) - emat_orig = e_new_scaled - # Now interpolate for the cases where S!=80 and/or J!=7 + # Now interpolate for the cases where S and/or J not the same in the + # country parameterization as in the default USA parameterization if ( - S == 80 + S == usa_params.S and np.array_equal( - np.squeeze(abil_wgts), - np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01]), + usa_params.lambdas, + lambdas, ) is True ): - emat_new_scaled = emat_orig + pass # will return the e_new_scaled found above since dims the same else: - # generate abil_midp vector - J = abil_wgts.shape[0] + # generate vector of mid points for the Malaysian ability groups abil_midp = np.zeros(J) pct_lb = 0.0 for j in range(J): - abil_midp[j] = pct_lb + 0.5 * abil_wgts[j] - pct_lb += abil_wgts[j] + abil_midp[j] = pct_lb + 0.5 * lambdas[j] + pct_lb += lambdas[j] + # generate vector of mid points for the USA ability groups + M = usa_params.lambdas.shape[0] + emat_j_midp = np.zeros(M) + pct_lb = 0.0 + for m in range(M): + emat_j_midp[m] = pct_lb + 0.5 * usa_params.lambdas[m] + pct_lb += usa_params.lambdas[m] # Make sure that values in abil_midp are within interpolating - # bounds set by the hard coded abil_wgts_orig - if abil_midp.min() < 0.125 or abil_midp.max() > 0.995: + # bounds + if ( + abil_midp.min() < emat_j_midp.min() + or abil_midp.max() > (1- usa_params.lambdas[-1]) + ): err = ( - "One or more entries in abils vector is outside the " - + "allowable bounds." + "One or more entries in abilities vector (lambdas) is outside the " + + "allowable bounds for interpolation." ) raise RuntimeError(err) - - emat_j_midp = np.array( - [0.125, 0.375, 0.600, 0.750, 0.850, 0.945, 0.995] + usa_step = 80 / usa_params.S + emat_s_midp = np.linspace( + usa_params.E + 0.5 * usa_step, + usa_params.E + usa_params.S - 0.5 * usa_step, + usa_params.S, ) - emat_s_midp = np.linspace(20.5, 99.5, 80) emat_j_mesh, emat_s_mesh = np.meshgrid(emat_j_midp, emat_s_midp) newstep = 80 / S - new_s_midp = np.linspace(20 + 0.5 * newstep, 100 - 0.5 * newstep, S) + new_s_midp = np.linspace(E + 0.5 * newstep, E + S - 0.5 * newstep, S) new_j_mesh, new_s_mesh = np.meshgrid(abil_midp, new_s_midp) newcoords = np.hstack( ( - emat_s_mesh.reshape((80 * 7, 1)), - emat_j_mesh.reshape((80 * 7, 1)), + emat_s_mesh.reshape((usa_params.S * usa_params.J, 1)), + emat_j_mesh.reshape((usa_params.S * usa_params.J, 1)), ) ) emat_new = si.griddata( newcoords, - emat_orig.flatten(), + emat_new_scaled.flatten(), (new_s_mesh, new_j_mesh), method="linear", ) emat_new_scaled = ( emat_new - / ( - emat_new * age_wgts.reshape(S, 1) * abil_wgts.reshape(1, J) - ).sum() + / (emat_new * age_wgts.reshape(S, 1) * lambdas.reshape(1, J)).sum() ) if plot: @@ -316,171 +190,10 @@ def f( pp.plot_income_data( new_s_midp, abil_midp, - abil_wgts, + lambdas, emat_new_scaled, OUTPUT_DIR, **kwargs, ) return emat_new_scaled - - -def get_e_orig(age_wgts, abil_wgts, plot=False): - r""" - This function generates the 80 x 7 matrix of lifetime earnings - ability profiles, corresponding to annual ages from 21 to 100 and to - paths based on income percentiles 0-25, 25-50, 50-70, 70-80, 80-90, - 90-99, 99-100. The ergodic population distribution is an input in - order to rescale the paths so that the weighted average equals 1. - - The data come from the following file: - - `data/ability/FR_wage_profile_tables.xlsx` - - The polynomials are of the form - - .. math:: - \ln(abil) = \alpha + \beta_{1}\text{age} + \beta_{2}\text{age}^2 - + \beta_{3}\text{age}^3 - - Values come from regression analysis using IRS CWHS with hours - imputed from the CPS. - - Args: - age_wgts (Numpy array): ergodic age distribution, length S - abil_wgts (Numpy array): population weights in each lifetime - earnings group, length J - plot (bool): if True, generates 3D plots of ability paths - - Returns: - e_orig_scaled (Numpy array): = lifetime ability profiles scaled - so that population-weighted average is 1, size SxJ - - """ - # Return and error if age_wgts is not a vector of size (80,) - if age_wgts.shape[0] != 80: - err = "Vector age_wgts does not have 80 elements." - raise RuntimeError(err) - # Return and error if abil_wgts is not a vector of size (7,) - if abil_wgts.shape[0] != 7: - err = "Vector abil_wgts does not have 7 elements." - raise RuntimeError(err) - - # 1) Generate polynomials and use them to get income profiles for - # ages 21 to 80. - one = np.array( - [ - -0.09720122, - 0.05995294, - 0.17654618, - 0.21168263, - 0.21638731, - 0.04500235, - 0.09229392, - ] - ) - two = np.array( - [ - 0.00247639, - -0.00004086, - -0.00240656, - -0.00306555, - -0.00321041, - 0.00094253, - 0.00012902, - ] - ) - three = np.array( - [ - -0.00001842, - -0.00000521, - 0.00001039, - 0.00001438, - 0.00001579, - -0.00001470, - -0.00001169, - ] - ) - const = np.array( - [ - 3.41e00, - 0.69689692, - -0.78761958, - -1.11e00, - -0.93939272, - 1.60e00, - 1.89e00, - ] - ) - ages_short = np.tile(np.linspace(21, 80, 60).reshape((60, 1)), (1, 7)) - log_abil_paths = ( - const - + (one * ages_short) - + (two * (ages_short**2)) - + (three * (ages_short**3)) - ) - abil_paths = np.exp(log_abil_paths) - e_orig = np.zeros((80, 7)) - e_orig[:60, :] = abil_paths - e_orig[60:, :] = 0.0 - - # 2) Forecast (with some art) the path of the final 20 years of - # ability types. This following variable is what percentage of - # ability at age 80 ability falls to at age 100. In general, we - # wanted people to lose half of their ability over a 20-year - # period. The first entry is 0.47, though, because nothing higher - # would converge. The second-to-last is 0.7 because this group - # actually has a slightly higher ability at age 80 than the last - # group, so this value makes it decrease more so it ends up being - # monotonic. - abil_deprec = np.array([0.47, 0.5, 0.5, 0.5, 0.5, 0.7, 0.5]) - # Initial guesses for the arctan. They're pretty sensitive. - init_guesses = np.array( - [ - [58, 0.0756438545595, -5.6940142786], - [27, 0.069, -5], - [35, 0.06, -5], - [37, 0.339936555352, -33.5987329144], - [70.5229181668, 0.0701993896947, -6.37746859905], - [35, 0.06, -5], - [35, 0.06, -5], - ] - ) - for j in range(7): - e_orig[60:, j] = arctan_fit( - e_orig[59, j], - one[j], - two[j], - three[j], - abil_deprec[j], - init_guesses[j], - ) - - # 3) Rescale the lifetime earnings path matrix so that the - # population weighted average equals 1. - e_orig_scaled = ( - e_orig - / (e_orig * age_wgts.reshape(80, 1) * abil_wgts.reshape(1, 7)).sum() - ) - - if plot: - ages_long = np.linspace(21, 100, 80) - abil_midp = np.array([12.5, 37.5, 60.0, 75.0, 85.0, 94.5, 99.5]) - # Plot original unscaled 80 x 7 ability matrix - kwargs = {"filesuffix": "_orig_unscaled"} - pp.plot_income_data( - ages_long, abil_midp, abil_wgts, e_orig, OUTPUT_DIR, **kwargs - ) - - # Plot original scaled 80 x 7 ability matrix - kwargs = {"filesuffix": "_orig_scaled"} - pp.plot_income_data( - ages_long, - abil_midp, - abil_wgts, - e_orig_scaled, - OUTPUT_DIR, - **kwargs, - ) - - return e_orig_scaled From cf25402d44845382647aa990d0d03dccd0128ed0 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Thu, 30 Nov 2023 12:40:40 -0500 Subject: [PATCH 05/11] UN data portal work around --- ogmys/demographics.py | 28 +++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/ogmys/demographics.py b/ogmys/demographics.py index 75c8d6d..0091cc6 100755 --- a/ogmys/demographics.py +++ b/ogmys/demographics.py @@ -12,6 +12,9 @@ import pandas as pd import matplotlib.pyplot as plt from ogcore import parameter_plots as pp +from ogmys.utils import get_legacy_session +from io import StringIO + UN_COUNTRY_CODE = "458" # UN code for MYS # create output director for figures @@ -57,9 +60,15 @@ def get_un_data( ) # get data from url - response = requests.get(target) - # Converts call into JSON - j = response.json() + response = get_legacy_session().get(target) + # Check if the request was successful before processing + if response.status_code == 200: + # Converts call into JSON + j = response.json() + else: + print( + f"Failed to retrieve population data. HTTP status code: {response.status_code}" + ) # Convert JSON into a pandas DataFrame. # pd.json_normalize flattens the JSON to accommodate nested lists # within the JSON structure @@ -69,9 +78,15 @@ def get_un_data( # Reset the target to the next page target = j["nextPage"] # call the API for the next page - response = requests.get(target) + response = get_legacy_session().get(target) # Convert response to JSON format - j = response.json() + if response.status_code == 200: + # Converts call into JSON + j = response.json() + else: + print( + f"Failed to retrieve population data. HTTP status code: {response.status_code}" + ) # Store the next page in a data frame df_temp = pd.json_normalize(j["data"]) # Append next page to the data frame @@ -679,8 +694,7 @@ def get_pop_objs( "omega": omega_path_S.T, "g_n_ss": g_n_SS, "omega_SS": omega_SSfx[-S:] / omega_SSfx[-S:].sum(), - "surv_rate": 1 - mort_rates_S, - "rho": mort_rates_S, + "rho": [mort_rates_S], "g_n": g_n_path, "imm_rates": imm_rates_mat.T, "omega_S_preTP": omega_S_preTP, From b145c52840cc1c4f3e57a6f06ad9374abde66c58 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Thu, 30 Nov 2023 12:40:58 -0500 Subject: [PATCH 06/11] response utility --- ogmys/utils.py | 31 +++++++++++++++++++++++++++++++ 1 file changed, 31 insertions(+) create mode 100644 ogmys/utils.py diff --git a/ogmys/utils.py b/ogmys/utils.py new file mode 100644 index 0000000..098f3ff --- /dev/null +++ b/ogmys/utils.py @@ -0,0 +1,31 @@ +import requests +import urllib3 +import ssl + + +class CustomHttpAdapter(requests.adapters.HTTPAdapter): + """ + The UN Data Portal server doesn't support "RFC 5746 secure renegotiation". This causes and error when the client is using OpenSSL 3, which enforces that standard by default. + The fix is to create a custom SSL context that allows for legacy connections. This defines a function get_legacy_session() that should be used instead of requests(). + """ + + # "Transport adapter" that allows us to use custom ssl_context. + def __init__(self, ssl_context=None, **kwargs): + self.ssl_context = ssl_context + super().__init__(**kwargs) + + def init_poolmanager(self, connections, maxsize, block=False): + self.poolmanager = urllib3.poolmanager.PoolManager( + num_pools=connections, + maxsize=maxsize, + block=block, + ssl_context=self.ssl_context, + ) + + +def get_legacy_session(): + ctx = ssl.create_default_context(ssl.Purpose.SERVER_AUTH) + ctx.options |= 0x4 # OP_LEGACY_SERVER_CONNECT #in Python 3.12 you will be able to switch from 0x4 to ssl.OP_LEGACY_SERVER_CONNECT. + session = requests.session() + session.mount("https://", CustomHttpAdapter(ctx)) + return session From 33c04966cf1239299d50f3cfdfddbd2cb599d394 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Thu, 30 Nov 2023 12:41:16 -0500 Subject: [PATCH 07/11] update income test --- ogmys/tests/test_income.py | 2340 +----------------------------------- 1 file changed, 5 insertions(+), 2335 deletions(-) diff --git a/ogmys/tests/test_income.py b/ogmys/tests/test_income.py index eccd6a7..ccbae65 100644 --- a/ogmys/tests/test_income.py +++ b/ogmys/tests/test_income.py @@ -7,2338 +7,6 @@ from ogmys import income -def test_arctan_func(): - """ - Test of arctan_func() - """ - expected_vals = np.array([0.14677821, 0.083305594, 0.057901228]) - xvals = np.array([1, 2, 3]) - a = 1.3 - b = 2.2 - c = 0.5 - test_vals = income.arctan_func(xvals, a, b, c) - - assert np.allclose(test_vals, expected_vals) - - -def test_arctan_deriv_func(): - """ - Test of arctan_deriv_func() - """ - expected_vals = np.array([-0.109814991, -0.036400091, -0.017707961]) - xvals = np.array([1, 2, 3]) - a = 1.3 - b = 2.2 - c = 0.5 - test_vals = income.arctan_deriv_func(xvals, a, b, c) - - assert np.allclose(test_vals, expected_vals) - - -def test_arc_error(): - """ - Test of arc_error() - """ - expected_vals = np.array([30.19765553, -1.40779391, 14.19212336]) - a = 1.3 - b = 2.2 - c = 0.5 - abc_vals = (a, b, c) - first_point = 30.2 - coef1 = 0.05995294 - coef2 = -0.00004086 - coef3 = -0.00000521 - abil_deprec = 0.47 - params = (first_point, coef1, coef2, coef3, abil_deprec) - test_vals = income.arc_error(abc_vals, params) - - assert np.allclose(test_vals, expected_vals) - - -@pytest.mark.local -def test_arctan_fit(): - """ - Test arctan_fit() function - """ - expected_vals = np.array( - [ - 30.19999399, - 30.19998699, - 30.19997918, - 30.19997039, - 30.19996043, - 30.19994904, - 30.1999359, - 30.19992057, - 30.19990246, - 30.19988072, - 30.19985415, - 30.19982094, - 30.19977824, - 30.19972131, - 30.1996416, - 30.19952204, - 30.19932277, - 30.19892423, - 30.19772859, - 14.19399974, - ] - ) - a = 1.3 - b = 2.2 - c = 0.5 - init_guesses = (a, b, c) - first_point = 30.2 - coef1 = 0.05995294 - coef2 = -0.00004086 - coef3 = -0.00000521 - abil_deprec = 0.47 - test_vals = income.arctan_fit( - first_point, coef1, coef2, coef3, abil_deprec, init_guesses - ) - assert np.allclose(test_vals, expected_vals) - - -def test_get_e_orig(): - """ - Test get_e_orig() function - """ - expected_vals = np.array( - [ - [ - 0.41637813, - 0.27892939, - 0.29770352, - 0.34999537, - 0.43550237, - 0.7104421, - 1.84111771, - ], - [ - 0.40966052, - 0.29351488, - 0.32491809, - 0.38673085, - 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1.03495944, - 1.14433385, - 1.22730141, - 1.32640565, - ], - [ - 0.1584293, - 0.28104019, - 0.58718372, - 0.90005352, - 1.09663915, - 1.20792244, - 1.29122983, - ], - [ - 0.15416985, - 0.27376772, - 0.54563088, - 0.74681858, - 1.05112886, - 1.18914593, - 1.25787151, - ], - ] - ) - - age_wgts = np.ones(80) * 1 / 80 - abil_wgts = np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.014]) - test_vals = income.get_e_orig(age_wgts, abil_wgts, plot=True) - - assert np.allclose(test_vals, expected_vals) - - -def test_get_e_orig_exception1(): - """ - Test that RuntimeError is raised if age_wgts does not have length 80 - """ - age_wgts = np.ones(70) * 1 / 80 - abil_wgts = np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.014]) - with pytest.raises(RuntimeError): - income.get_e_orig(age_wgts, abil_wgts) - - -def test_get_e_orig_exception2(): - """ - Test that RuntimeError is raised if abil_wgts does not have length 87 - """ - age_wgts = np.ones(80) * 1 / 80 - abil_wgts = np.array([0.25, 0.25, 0.2, 0.1, 0.1]) - with pytest.raises(RuntimeError): - income.get_e_orig(age_wgts, abil_wgts) - - -abil_wgts1 = np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01]) -expected_vals1 = np.array( - [ - [ - 0.42494207, - 0.28466632, - 0.3038266, - 0.35719397, - 0.44445965, - 0.72505425, - 1.87898526, - ], - [ - 0.4180863, - 0.29955181, - 0.33160091, - 0.39468501, - 0.49132198, - 0.77385826, - 2.03879639, - ], - [ - 0.41237957, - 0.31497323, - 0.36067077, - 0.43426898, - 0.54077514, - 0.82590159, - 2.2093588, - ], - [ - 0.40773253, - 0.33092349, - 0.39096563, - 0.47584565, - 0.59268652, - 0.88131842, - 2.39094783, - ], - [ - 0.40406625, - 0.34739233, - 0.42240171, - 0.51929006, - 0.64689283, - 0.94023572, - 2.58377647, - ], - [ - 0.40131077, - 0.36436611, - 0.45488256, - 0.56445325, - 0.70320139, - 1.00277084, - 2.7879842, - ], - [ - 0.39940367, - 0.38182754, - 0.48829978, - 0.61116362, - 0.76139208, - 1.06902877, - 3.00362525, - ], - [ - 0.398289, - 0.39975553, - 0.52253387, - 0.65922856, - 0.82121973, - 1.13909917, - 3.23065656, - ], - [ - 0.39791616, - 0.41812496, - 0.5574554, - 0.70843665, - 0.88241713, - 1.2130531, - 3.46892559, - ], - [ - 0.39823907, - 0.43690651, - 0.59292619, - 0.75856008, - 0.94469837, - 1.2909396, - 3.71815816, - ], - [ - 0.39921529, - 0.45606648, - 0.62880075, - 0.80935745, - 1.00776257, - 1.37278196, - 3.97794668, - ], - [ - 0.40080534, - 0.47556672, - 0.66492774, - 0.86057684, - 1.0712979, - 1.45857386, - 4.24773894, - ], - [ - 0.40297197, - 0.49536444, - 0.70115155, - 0.91195898, - 1.13498573, - 1.54827537, - 4.52682784, - ], - [ - 0.40567964, - 0.51541221, - 0.737314, - 0.96324062, - 1.19850488, - 1.64180888, - 4.81434242, - ], - [ - 0.40889388, - 0.53565788, - 0.77325593, - 1.01415791, - 1.26153591, - 1.73905494, - 5.10924057, - ], - [ - 0.41258082, - 0.55604462, - 0.80881894, - 1.06444974, - 1.32376526, - 1.83984827, - 5.4103037, - ], - [ - 0.4167067, - 0.57651095, - 0.84384702, - 1.11386097, - 1.38488923, - 1.94397384, - 5.71613385, - ], - [ - 0.42123744, - 0.59699085, - 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1.22758655, - 1.31225013, - ], - [ - 0.1688341, - 0.30892323, - 0.55451336, - 0.75897625, - 1.06824048, - 1.20850438, - 1.27834876, - ], - ] -) -abil_wgts3 = np.array( - [0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.005, 0.004, 0.0009, 0.0001] -) -expected_vals3 = np.zeros((80, 10)) -expected_vals3[:, :7] = expected_vals1.copy() -old_vals = expected_vals1[:, 6].copy() -expected_vals3[:, 5] = expected_vals1[:, -2] * 1.25 -expected_vals3[:, 6] = old_vals * 0.458759521 * 2.75 -expected_vals3[:, 7] = old_vals * 0.847252448 * 3.5 -expected_vals3[:, 8] = old_vals * 2.713698465 * 3.5 -expected_vals3[:, 9] = old_vals * 18.74863983 * 4.0 -age_wgts = np.ones(80) * 1 / 80 -expected_vals3 = ( - expected_vals3 - / ( - expected_vals3 * age_wgts.reshape(80, 1) * abil_wgts3.reshape(1, 10) - ).sum() -) - -abil_wgts4 = np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.005, 0.004, 0.001]) -expected_vals4 = np.zeros((80, 9)) -expected_vals4[:, :7] = expected_vals1.copy() -old_vals = expected_vals1[:, 6].copy() -expected_vals4[:, 6] = old_vals * 0.458759521 -expected_vals4[:, 7] = old_vals * 0.847252448 -expected_vals4[:, 8] = old_vals * 4.317192601 -expected_vals4 = ( - expected_vals4 - / ( - expected_vals4 * age_wgts.reshape(80, 1) * abil_wgts4.reshape(1, 9) - ).sum() -) - - # @pytest.mark.parametrize( # "abil_wgts,expected_vals", # [ @@ -2354,7 +22,7 @@ def test_get_e_orig_exception2(): # "J=9 weights", # ], # ) -# Commenting out for now - need to update values above for MYS and +# Commenting out for now - need to update values above for IND and # there's probably a better way to do this # def test_get_e_interp(abil_wgts, expected_vals): # """ @@ -2373,6 +41,8 @@ def test_get_e_interp_exception(): Test that RuntimeError is abil_wgts not suitable for interpolation """ age_wgts = np.ones(80) * 1 / 80 - abil_wgts = np.array([0.1, 0.35, 0.2, 0.1, 0.1, 0.09, 0.01]) + abil_wgts = np.array( + [0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.009999, 0.000001] + ) with pytest.raises(RuntimeError): - income.get_e_interp(80, age_wgts, age_wgts, abil_wgts) + income.get_e_interp(20, 80, 8, abil_wgts, age_wgts) From 0f4c4e46bf9d6e01d8cef6ecdc6fcff1844e8bf0 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Thu, 30 Nov 2023 12:46:22 -0500 Subject: [PATCH 08/11] format --- ogmys/calibrate.py | 1 - ogmys/income.py | 5 ++--- ogmys/tests/test_income.py | 4 +--- 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/ogmys/calibrate.py b/ogmys/calibrate.py index 867644c..a581c65 100644 --- a/ogmys/calibrate.py +++ b/ogmys/calibrate.py @@ -22,7 +22,6 @@ def __init__( client=None, num_workers=1, ): - self.estimate_tax_functions = estimate_tax_functions self.estimate_beta = estimate_beta self.estimate_chi_n = estimate_chi_n diff --git a/ogmys/income.py b/ogmys/income.py index c75df9c..e5d9572 100644 --- a/ogmys/income.py +++ b/ogmys/income.py @@ -149,9 +149,8 @@ def f( # Make sure that values in abil_midp are within interpolating # bounds - if ( - abil_midp.min() < emat_j_midp.min() - or abil_midp.max() > (1- usa_params.lambdas[-1]) + if abil_midp.min() < emat_j_midp.min() or abil_midp.max() > ( + 1 - usa_params.lambdas[-1] ): err = ( "One or more entries in abilities vector (lambdas) is outside the " diff --git a/ogmys/tests/test_income.py b/ogmys/tests/test_income.py index ccbae65..fef5c28 100644 --- a/ogmys/tests/test_income.py +++ b/ogmys/tests/test_income.py @@ -41,8 +41,6 @@ def test_get_e_interp_exception(): Test that RuntimeError is abil_wgts not suitable for interpolation """ age_wgts = np.ones(80) * 1 / 80 - abil_wgts = np.array( - [0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.009999, 0.000001] - ) + abil_wgts = np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.009999, 0.000001]) with pytest.raises(RuntimeError): income.get_e_interp(20, 80, 8, abil_wgts, age_wgts) From 3b84f3ad319fb075a217c8c1ba09db0f05e54da5 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Fri, 1 Dec 2023 13:42:38 -0500 Subject: [PATCH 09/11] update build and test workflow --- .github/workflows/build_and_test.yml | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build_and_test.yml b/.github/workflows/build_and_test.yml index fba6501..e1e1c8b 100644 --- a/.github/workflows/build_and_test.yml +++ b/.github/workflows/build_and_test.yml @@ -1,4 +1,4 @@ -name: Build and test [Python 3.7, 3.8, 3.9] +name: Build and test [Python 3.8, 3.9, 3.10] on: [push, pull_request] @@ -7,7 +7,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.7, 3.8, 3.9] + python-version: [3.8, 3.9, "3.10"] steps: - name: Checkout @@ -16,8 +16,10 @@ jobs: persist-credentials: false - name: Setup Miniconda using Python ${{ matrix.python-version }} - uses: conda-incubator/setup-miniconda@v2 + uses: conda-incubator/setup-miniconda@v4 with: + miniforge-variant: Mambaforge + auto-update-conda: true activate-environment: ogmys-dev environment-file: environment.yml python-version: ${{ matrix.python-version }} @@ -35,7 +37,7 @@ jobs: run: | pytest -m 'not local and not regression' --cov=./ --cov-report=xml - name: Upload coverage to Codecov - uses: codecov/codecov-action@v2 + uses: codecov/codecov-action@v3 with: files: ./coverage.xml flags: unittests From 1072dd00ef9b3226095d1081654169bbd4c75db2 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Fri, 1 Dec 2023 14:04:11 -0500 Subject: [PATCH 10/11] fix gh action versions --- .github/workflows/build_and_test.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_and_test.yml b/.github/workflows/build_and_test.yml index e1e1c8b..37df936 100644 --- a/.github/workflows/build_and_test.yml +++ b/.github/workflows/build_and_test.yml @@ -11,12 +11,12 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v2 + uses: actions/checkout@v4 with: persist-credentials: false - name: Setup Miniconda using Python ${{ matrix.python-version }} - uses: conda-incubator/setup-miniconda@v4 + uses: conda-incubator/setup-miniconda@v2 with: miniforge-variant: Mambaforge auto-update-conda: true From 9bb37ea07c61ba56668f41808d7d68fce6f3f241 Mon Sep 17 00:00:00 2001 From: jdebacker Date: Fri, 1 Dec 2023 14:35:56 -0500 Subject: [PATCH 11/11] update env --- environment.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/environment.yml b/environment.yml index 9b99e76..94c5c92 100644 --- a/environment.yml +++ b/environment.yml @@ -2,12 +2,11 @@ name: ogmys-dev channels: - conda-forge dependencies: -- python>=3.7.7, <3.11 # This restriction can be removed as soon as these packages support Python 3.11 +- python>=3.7.7 - ipython - setuptools - psutil -- openssl=1.1.1 # This is a requirement of the UN data portal which we are using in demographics.py -- numpy<=1.21.2 # This restriction can be removed as soon as Numba supports NumPy 1.22 +- numpy - scipy>=1.7.1 - pandas>=1.2.5 - matplotlib