From 695b0e6d9ce65d2830e85c6357b46961d309a2bd Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
<66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 30 Oct 2023 18:22:34 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---
docs/projects/_numerical_integration.py | 1 -
respy/exogenous_processes.py | 1 -
respy/likelihood.py | 2 --
respy/shared.py | 1 -
respy/solve.py | 1 -
respy/tests/_former_code.py | 24 ------------------------
respy/tests/test_solve.py | 1 -
7 files changed, 31 deletions(-)
diff --git a/docs/projects/_numerical_integration.py b/docs/projects/_numerical_integration.py
index b1bfadc26..5ef9a8742 100644
--- a/docs/projects/_numerical_integration.py
+++ b/docs/projects/_numerical_integration.py
@@ -70,7 +70,6 @@ def get_rmse_rate(df, comparison_rates, methods):
- figure
"""
for measure in ["absolute", "relative"]:
-
fig, ax = plt.subplots(1, 1, figsize=(6.5, 4.5))
for m in methods:
diff --git a/respy/exogenous_processes.py b/respy/exogenous_processes.py
index f34b03c38..1a1a432de 100644
--- a/respy/exogenous_processes.py
+++ b/respy/exogenous_processes.py
@@ -39,7 +39,6 @@ def compute_transition_probabilities(
# Compute the probabilities for every exogenous process.
probabilities = []
for exog_proc in exogenous_processes:
-
# Create the dot product of covariates and parameters.
x_betas = [
pandas_dot(states[params.index], params)
diff --git a/respy/likelihood.py b/respy/likelihood.py
index 8c8947abf..588274ccc 100644
--- a/respy/likelihood.py
+++ b/respy/likelihood.py
@@ -460,7 +460,6 @@ def _simulate_log_probability_of_individuals_observed_choice(
smoothed_value_functions = np.empty(n_choices)
for i in range(n_draws):
-
for j in range(n_choices):
value_function, _ = aggregate_keane_wolpin_utility(
wages[j], nonpec[j], continuation_values[j], draws[i, j], delta
@@ -553,7 +552,6 @@ def _process_estimation_data(df, state_space, optim_paras, options):
def _update_optim_paras_with_initial_experience_levels(optim_paras, df):
"""Adjust the initial experience levels in optim_paras from the data."""
for choice in optim_paras["choices_w_exp"]:
-
# Adjust initial experience levels for all choices with experiences.
init_exp_data = np.sort(
df.query("Period == 0")[f"Experience_{choice.title()}"].unique()
diff --git a/respy/shared.py b/respy/shared.py
index d49722083..9bcbc16bb 100644
--- a/respy/shared.py
+++ b/respy/shared.py
@@ -493,7 +493,6 @@ def calculate_expected_value_functions(
expected_value_functions[0] = 0
for i in range(n_draws):
-
max_value_functions = 0
for j in range(n_choices):
diff --git a/respy/solve.py b/respy/solve.py
index 76550da14..5821d36c7 100644
--- a/respy/solve.py
+++ b/respy/solve.py
@@ -197,7 +197,6 @@ def _solve_with_backward_induction(state_space, optim_paras, options):
)
else:
-
wages = state_space.get_attribute_from_period("wages", period)
nonpecs = state_space.get_attribute_from_period("nonpecs", period)
continuation_values = state_space.get_continuation_values(period)
diff --git a/respy/tests/_former_code.py b/respy/tests/_former_code.py
index 4437f93f3..83cb22e42 100644
--- a/respy/tests/_former_code.py
+++ b/respy/tests/_former_code.py
@@ -72,7 +72,6 @@ def _create_state_space_kw94(n_periods, n_types, edu_starts, edu_max):
# Construct state space by periods
for period in range(n_periods):
-
# Build periodic indexer.
max_edu_starts = max(edu_starts)
dim_edu = min(max_edu_starts + period, edu_max) + 1
@@ -82,10 +81,8 @@ def _create_state_space_kw94(n_periods, n_types, edu_starts, edu_max):
# Loop over all unobserved types
for type_ in range(n_types):
-
# Loop overall all initial levels of schooling
for edu_start in edu_starts:
-
# For occupations and education it is necessary to loop over period
# + 1 as zero has to be included if it is never this choice and period
# + 1 if it is always the same choice.
@@ -95,22 +92,17 @@ def _create_state_space_kw94(n_periods, n_types, edu_starts, edu_max):
# Loop over all admissible work experiences for Occupation A
for exp_a in range(period + 1):
-
# Loop over all admissible work experience for Occupation B
for exp_b in range(period + 1 - exp_a):
-
# Loop over all admissible additional education levels
for edu_add in range(
min(period + 1 - exp_a - exp_b, edu_max + 1 - edu_start)
):
-
# Loop over all admissible values for the lagged activity:
# (1) Occupation A, (2) Occupation B, (3) Education, and (4)
# Home.
for lagged_choice in range(4):
-
if period > 0:
-
# (0, 1) Whenever an agent has only worked in
# Occupation A, then the lagged choice cannot be
# anything other than one.
@@ -231,7 +223,6 @@ def _create_state_space_kw97_base(n_periods, n_types, edu_starts, edu_max):
# Construct state space by periods
for period in range(n_periods):
-
# Build periodic indexer.
max_edu_starts = max(edu_starts)
dim_edu = min(max_edu_starts + period, edu_max) + 1
@@ -241,10 +232,8 @@ def _create_state_space_kw97_base(n_periods, n_types, edu_starts, edu_max):
# Loop over all unobserved types
for type_ in range(n_types):
-
# Loop overall all initial levels of schooling
for edu_start in edu_starts:
-
# For occupations and education it is necessary to loop over period
# + 1 as zero has to be included if it is never this choice and period
# + 1 if it is always the same choice.
@@ -254,13 +243,10 @@ def _create_state_space_kw97_base(n_periods, n_types, edu_starts, edu_max):
# Loop over all admissible work experiences for Occupation A
for exp_a in range(period + 1):
-
# Loop over all admissible work experience for Occupation B
for exp_b in range(period + 1 - exp_a):
-
# Loop over all admissible work experience for Occupation B
for exp_mil in range(period + 1 - exp_a - exp_b):
-
# Loop over all admissible additional education levels
for edu_add in range(
min(
@@ -268,7 +254,6 @@ def _create_state_space_kw97_base(n_periods, n_types, edu_starts, edu_max):
edu_max + 1 - edu_start,
)
):
-
# Continue if state still exist. This condition is
# only triggered by multiple initial levels of
# education.
@@ -336,7 +321,6 @@ def _create_state_space_kw97_extended(n_periods, n_types, edu_starts, edu_max):
# Construct state space by periods
for period in range(n_periods):
-
# Build periodic indexer.
max_edu_starts = max(edu_starts)
dim_edu = min(max_edu_starts + period, edu_max) + 1
@@ -346,10 +330,8 @@ def _create_state_space_kw97_extended(n_periods, n_types, edu_starts, edu_max):
# Loop over all unobserved types
for type_ in range(n_types):
-
# Loop overall all initial levels of schooling
for edu_start in edu_starts:
-
# For occupations and education it is necessary to loop over period
# + 1 as zero has to be included if it is never this choice and period
# + 1 if it is always the same choice.
@@ -359,13 +341,10 @@ def _create_state_space_kw97_extended(n_periods, n_types, edu_starts, edu_max):
# Loop over all admissible work experiences for Occupation A
for exp_a in range(period + 1):
-
# Loop over all admissible work experience for Occupation B
for exp_b in range(period + 1 - exp_a):
-
# Loop over all admissible work experience for Occupation B
for exp_mil in range(period + 1 - exp_a - exp_b):
-
# Loop over all admissible additional education levels
for edu_add in range(
min(
@@ -373,14 +352,11 @@ def _create_state_space_kw97_extended(n_periods, n_types, edu_starts, edu_max):
edu_max + 1 - edu_start,
)
):
-
# Loop over all admissible values for the lagged
# activity: (1) Occupation A, (2) Occupation B, (3)
# Military, (4) Education, and (5) Home.
for lagged_choice in range(5):
-
if period > 0:
-
# (0, 1) Whenever an agent has only worked in
# Occupation A, then the lagged choice cannot be
# anything other than one.
diff --git a/respy/tests/test_solve.py b/respy/tests/test_solve.py
index eca5a062d..7bd67e6b9 100644
--- a/respy/tests/test_solve.py
+++ b/respy/tests/test_solve.py
@@ -226,7 +226,6 @@ def test_create_state_space_vs_specialized_kw97(model):
]
for index in indexer.keys():
-
if index[0] == period:
assert list(index) in indices_old