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timothyb0912/pylogit | pylogit/choice_tools.py | create_row_to_some_id_col_mapping | def create_row_to_some_id_col_mapping(id_array):
"""
Parameters
----------
id_array : 1D ndarray.
All elements of the array should be ints representing some id related
to the corresponding row.
Returns
-------
rows_to_ids : 2D scipy sparse array.
Will map each row of... | python | def create_row_to_some_id_col_mapping(id_array):
"""
Parameters
----------
id_array : 1D ndarray.
All elements of the array should be ints representing some id related
to the corresponding row.
Returns
-------
rows_to_ids : 2D scipy sparse array.
Will map each row of... | [
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timothyb0912/pylogit | pylogit/choice_tools.py | create_sparse_mapping | def create_sparse_mapping(id_array, unique_ids=None):
"""
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value of the unique ids in id_array.
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id_array : 1D ndarray of ints.
E... | python | def create_sparse_mapping(id_array, unique_ids=None):
"""
Will create a scipy.sparse compressed-sparse-row matrix that maps
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id_array : 1D ndarray of ints.
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timothyb0912/pylogit | pylogit/choice_tools.py | create_long_form_mappings | def create_long_form_mappings(long_form,
obs_id_col,
alt_id_col,
choice_col=None,
nest_spec=None,
mix_id_col=None,
dense=False):
"""
... | python | def create_long_form_mappings(long_form,
obs_id_col,
alt_id_col,
choice_col=None,
nest_spec=None,
mix_id_col=None,
dense=False):
"""
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timothyb0912/pylogit | pylogit/choice_tools.py | convert_long_to_wide | def convert_long_to_wide(long_data,
ind_vars,
alt_specific_vars,
subset_specific_vars,
obs_id_col,
alt_id_col,
choice_col,
alt_name_dict=None,
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ind_vars,
alt_specific_vars,
subset_specific_vars,
obs_id_col,
alt_id_col,
choice_col,
alt_name_dict=None,
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timothyb0912/pylogit | pylogit/choice_tools.py | check_wide_data_for_blank_choices | def check_wide_data_for_blank_choices(choice_col, wide_data):
"""
Checks `wide_data` for null values in the choice column, and raises a
helpful ValueError if null values are found.
Parameters
----------
choice_col : str.
Denotes the column in `wide_data` that is used to record each
... | python | def check_wide_data_for_blank_choices(choice_col, wide_data):
"""
Checks `wide_data` for null values in the choice column, and raises a
helpful ValueError if null values are found.
Parameters
----------
choice_col : str.
Denotes the column in `wide_data` that is used to record each
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_unique_obs_ids_in_wide_data | def ensure_unique_obs_ids_in_wide_data(obs_id_col, wide_data):
"""
Ensures that there is one observation per row in wide_data. Raises a
helpful ValueError if otherwise.
Parameters
----------
obs_id_col : str.
Denotes the column in `wide_data` that contains the observation ID
val... | python | def ensure_unique_obs_ids_in_wide_data(obs_id_col, wide_data):
"""
Ensures that there is one observation per row in wide_data. Raises a
helpful ValueError if otherwise.
Parameters
----------
obs_id_col : str.
Denotes the column in `wide_data` that contains the observation ID
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_chosen_alternatives_are_in_user_alt_ids | def ensure_chosen_alternatives_are_in_user_alt_ids(choice_col,
wide_data,
availability_vars):
"""
Ensures that all chosen alternatives in `wide_df` are present in the
`availability_vars` dict. Raises a help... | python | def ensure_chosen_alternatives_are_in_user_alt_ids(choice_col,
wide_data,
availability_vars):
"""
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_each_wide_obs_chose_an_available_alternative | def ensure_each_wide_obs_chose_an_available_alternative(obs_id_col,
choice_col,
availability_vars,
wide_data):
"""
Checks whether or not each ob... | python | def ensure_each_wide_obs_chose_an_available_alternative(obs_id_col,
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_all_wide_alt_ids_are_chosen | def ensure_all_wide_alt_ids_are_chosen(choice_col,
alt_specific_vars,
availability_vars,
wide_data):
"""
Checks to make sure all user-specified alternative id's, both in
`alt_specific_vars` a... | python | def ensure_all_wide_alt_ids_are_chosen(choice_col,
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timothyb0912/pylogit | pylogit/choice_tools.py | ensure_contiguity_in_observation_rows | def ensure_contiguity_in_observation_rows(obs_id_vector):
"""
Ensures that all rows pertaining to a given choice situation are located
next to one another. Raises a helpful ValueError otherwise. This check is
needed because the hessian calculation function requires the design matrix
to have contigui... | python | def ensure_contiguity_in_observation_rows(obs_id_vector):
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timothyb0912/pylogit | pylogit/choice_tools.py | convert_wide_to_long | def convert_wide_to_long(wide_data,
ind_vars,
alt_specific_vars,
availability_vars,
obs_id_col,
choice_col,
new_alt_id_name=None):
"""
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choice_col,
new_alt_id_name=None):
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | relate_obs_ids_to_chosen_alts | def relate_obs_ids_to_chosen_alts(obs_id_array,
alt_id_array,
choice_array):
"""
Creates a dictionary that relates each unique alternative id to the set of
observations ids that chose the given alternative.
Parameters
----------
... | python | def relate_obs_ids_to_chosen_alts(obs_id_array,
alt_id_array,
choice_array):
"""
Creates a dictionary that relates each unique alternative id to the set of
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Parameters
----------
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | get_num_obs_choosing_each_alternative | def get_num_obs_choosing_each_alternative(obs_per_alt_dict):
"""
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observation that have chosen the given alternative (i.e. the associated
dictionary key). Will also determine the total number of unique
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... | python | def get_num_obs_choosing_each_alternative(obs_per_alt_dict):
"""
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | create_cross_sectional_bootstrap_samples | def create_cross_sectional_bootstrap_samples(obs_id_array,
alt_id_array,
choice_array,
num_samples,
seed=None):
"""
Determines the u... | python | def create_cross_sectional_bootstrap_samples(obs_id_array,
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choice_array,
num_samples,
seed=None):
"""
Determines the u... | [
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | create_bootstrap_id_array | def create_bootstrap_id_array(obs_id_per_sample):
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Creates a 2D ndarray that contains the 'bootstrap ids' for each replication
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Parameters
----------
obs_id_per_sample : 2D ndarray of ints.
Should have one row for ea... | python | def create_bootstrap_id_array(obs_id_per_sample):
"""
Creates a 2D ndarray that contains the 'bootstrap ids' for each replication
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | create_deepcopied_groupby_dict | def create_deepcopied_groupby_dict(orig_df, obs_id_col):
"""
Will create a dictionary where each key corresponds to a unique value in
`orig_df[obs_id_col]` and each value corresponds to all of the rows of
`orig_df` where `orig_df[obs_id_col] == key`.
Parameters
----------
orig_df : pandas D... | python | def create_deepcopied_groupby_dict(orig_df, obs_id_col):
"""
Will create a dictionary where each key corresponds to a unique value in
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | check_column_existence | def check_column_existence(col_name, df, presence=True):
"""
Checks whether or not `col_name` is in `df` and raises a helpful error msg
if the desired condition is not met.
Parameters
----------
col_name : str.
Should represent a column whose presence in `df` is to be checked.
df : ... | python | def check_column_existence(col_name, df, presence=True):
"""
Checks whether or not `col_name` is in `df` and raises a helpful error msg
if the desired condition is not met.
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col_name : str.
Should represent a column whose presence in `df` is to be checked.
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | ensure_resampled_obs_ids_in_df | def ensure_resampled_obs_ids_in_df(resampled_obs_ids, orig_obs_id_array):
"""
Checks whether all ids in `resampled_obs_ids` are in `orig_obs_id_array`.
Raises a helpful ValueError if not.
Parameters
----------
resampled_obs_ids : 1D ndarray of ints.
Should contain the observation ids of... | python | def ensure_resampled_obs_ids_in_df(resampled_obs_ids, orig_obs_id_array):
"""
Checks whether all ids in `resampled_obs_ids` are in `orig_obs_id_array`.
Raises a helpful ValueError if not.
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timothyb0912/pylogit | pylogit/bootstrap_sampler.py | create_bootstrap_dataframe | def create_bootstrap_dataframe(orig_df,
obs_id_col,
resampled_obs_ids_1d,
groupby_dict,
boot_id_col="bootstrap_id"):
"""
Will create the altered dataframe of data needed to estimate a choi... | python | def create_bootstrap_dataframe(orig_df,
obs_id_col,
resampled_obs_ids_1d,
groupby_dict,
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timothyb0912/pylogit | pylogit/bootstrap.py | get_param_names | def get_param_names(model_obj):
"""
Extracts all the names to be displayed for the estimated parameters.
Parameters
----------
model_obj : an instance of an MNDC object.
Should have the following attributes:
`['ind_var_names', 'intercept_names', 'shape_names', 'nest_names']`.
R... | python | def get_param_names(model_obj):
"""
Extracts all the names to be displayed for the estimated parameters.
Parameters
----------
model_obj : an instance of an MNDC object.
Should have the following attributes:
`['ind_var_names', 'intercept_names', 'shape_names', 'nest_names']`.
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timothyb0912/pylogit | pylogit/bootstrap.py | get_param_list_for_prediction | def get_param_list_for_prediction(model_obj, replicates):
"""
Create the `param_list` argument for use with `model_obj.predict`.
Parameters
----------
model_obj : an instance of an MNDC object.
Should have the following attributes:
`['ind_var_names', 'intercept_names', 'shape_names'... | python | def get_param_list_for_prediction(model_obj, replicates):
"""
Create the `param_list` argument for use with `model_obj.predict`.
Parameters
----------
model_obj : an instance of an MNDC object.
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timothyb0912/pylogit | pylogit/bootstrap.py | Boot.generate_bootstrap_replicates | def generate_bootstrap_replicates(self,
num_samples,
mnl_obj=None,
mnl_init_vals=None,
mnl_fit_kwargs=None,
extract_init_vals=None... | python | def generate_bootstrap_replicates(self,
num_samples,
mnl_obj=None,
mnl_init_vals=None,
mnl_fit_kwargs=None,
extract_init_vals=None... | [
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timothyb0912/pylogit | pylogit/bootstrap.py | Boot.generate_jackknife_replicates | def generate_jackknife_replicates(self,
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extract_init_vals=None,
print_res=F... | python | def generate_jackknife_replicates(self,
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print_res=F... | [
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timothyb0912/pylogit | pylogit/bootstrap.py | Boot.calc_log_likes_for_replicates | def calc_log_likes_for_replicates(self,
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seed=None):
"""
Calculate the log-likelihood value of one's replicates, given one's
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... | python | def calc_log_likes_for_replicates(self,
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seed=None):
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Calculate the log-likelihood value of one's replicates, given one's
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timothyb0912/pylogit | pylogit/bootstrap.py | Boot.calc_gradient_norm_for_replicates | def calc_gradient_norm_for_replicates(self,
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timothyb0912/pylogit | pylogit/bootstrap.py | Boot.calc_percentile_interval | def calc_percentile_interval(self, conf_percentage):
"""
Calculates percentile bootstrap confidence intervals for one's model.
Parameters
----------
conf_percentage : scalar in the interval (0.0, 100.0).
Denotes the confidence-level for the returned endpoints. For
... | python | def calc_percentile_interval(self, conf_percentage):
"""
Calculates percentile bootstrap confidence intervals for one's model.
Parameters
----------
conf_percentage : scalar in the interval (0.0, 100.0).
Denotes the confidence-level for the returned endpoints. For
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timothyb0912/pylogit | pylogit/bootstrap.py | Boot.calc_bca_interval | def calc_bca_interval(self, conf_percentage):
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Parameters
----------
conf_percentage : scalar in the interval (0.0, 100.0).
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"""
Calculates Bias-Corrected and Accelerated (BCa) Bootstrap Confidence
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timothyb0912/pylogit | pylogit/bootstrap.py | Boot.calc_abc_interval | def calc_abc_interval(self,
conf_percentage,
init_vals,
epsilon=0.001,
**fit_kwargs):
"""
Calculates Approximate Bootstrap Confidence Intervals for one's model.
Parameters
----------
... | python | def calc_abc_interval(self,
conf_percentage,
init_vals,
epsilon=0.001,
**fit_kwargs):
"""
Calculates Approximate Bootstrap Confidence Intervals for one's model.
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timothyb0912/pylogit | pylogit/bootstrap.py | Boot.calc_conf_intervals | def calc_conf_intervals(self,
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interval_type='all',
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epsilon=abc.EPSILON,
**fit_kwargs):
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timothyb0912/pylogit | pylogit/clog_log.py | split_param_vec | def split_param_vec(param_vec, rows_to_alts, design, return_all_types=False):
"""
Parameters
----------
param_vec : 1D ndarray.
Elements should all be ints, floats, or longs. Should have as many
elements as there are parameters being estimated.
rows_to_alts : 2D scipy sparse matrix.
... | python | def split_param_vec(param_vec, rows_to_alts, design, return_all_types=False):
"""
Parameters
----------
param_vec : 1D ndarray.
Elements should all be ints, floats, or longs. Should have as many
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rows_to_alts : 2D scipy sparse matrix.
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timothyb0912/pylogit | pylogit/clog_log.py | _cloglog_utility_transform | def _cloglog_utility_transform(systematic_utilities,
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intercept_ref_pos=None,
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shape_params,
output_array=None,
*args, **kwargs):
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Parameters
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... | python | def _cloglog_transform_deriv_v(systematic_utilities,
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timothyb0912/pylogit | pylogit/clog_log.py | create_calc_dh_d_alpha | def create_calc_dh_d_alpha(estimator):
"""
Return the function that can be used in the various gradient and hessian
calculations to calculate the derivative of the transformation with respect
to the outside intercept parameters.
Parameters
----------
estimator : an instance of the estimatio... | python | def create_calc_dh_d_alpha(estimator):
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Return the function that can be used in the various gradient and hessian
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timothyb0912/pylogit | pylogit/estimation.py | calc_individual_chi_squares | def calc_individual_chi_squares(residuals,
long_probabilities,
rows_to_obs):
"""
Calculates individual chi-squared values for each choice situation in the
dataset.
Parameters
----------
residuals : 1D ndarray.
The choice ve... | python | def calc_individual_chi_squares(residuals,
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rows_to_obs):
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Calculates individual chi-squared values for each choice situation in the
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timothyb0912/pylogit | pylogit/estimation.py | calc_rho_and_rho_bar_squared | def calc_rho_and_rho_bar_squared(final_log_likelihood,
null_log_likelihood,
num_est_parameters):
"""
Calculates McFadden's rho-squared and rho-bar squared for the given model.
Parameters
----------
final_log_likelihood : float.
... | python | def calc_rho_and_rho_bar_squared(final_log_likelihood,
null_log_likelihood,
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"""
Calculates McFadden's rho-squared and rho-bar squared for the given model.
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timothyb0912/pylogit | pylogit/estimation.py | calc_and_store_post_estimation_results | def calc_and_store_post_estimation_results(results_dict,
estimator):
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timothyb0912/pylogit | pylogit/estimation.py | estimate | def estimate(init_values,
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just_point=False,
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timothyb0912/pylogit | pylogit/estimation.py | EstimationObj.convenience_split_params | def convenience_split_params(self, params, return_all_types=False):
"""
Splits parameter vector into shape, intercept, and index parameters.
Parameters
----------
params : 1D ndarray.
The array of parameters being estimated or used in calculations.
return_all... | python | def convenience_split_params(self, params, return_all_types=False):
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Splits parameter vector into shape, intercept, and index parameters.
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params : 1D ndarray.
The array of parameters being estimated or used in calculations.
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timothyb0912/pylogit | pylogit/estimation.py | EstimationObj.calc_neg_log_likelihood_and_neg_gradient | def calc_neg_log_likelihood_and_neg_gradient(self, params):
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"""
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"""
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timothyb0912/pylogit | pylogit/estimation.py | LogitTypeEstimator.convenience_calc_hessian | def convenience_calc_hessian(self, params):
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timothyb0912/pylogit | pylogit/estimation.py | LogitTypeEstimator.convenience_calc_fisher_approx | def convenience_calc_fisher_approx(self, params):
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timothyb0912/pylogit | pylogit/conditional_logit.py | split_param_vec | def split_param_vec(beta,
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Parameters
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beta : 1D ndarray.
All elements should by ints, floats, or longs. Should have 1 element
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design=None,
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*args, **kwargs):
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beta : 1D ndarray.
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timothyb0912/pylogit | pylogit/conditional_logit.py | _mnl_utility_transform | def _mnl_utility_transform(systematic_utilities, *args, **kwargs):
"""
Parameters
----------
systematic_utilities : 1D ndarray.
Should contain the systematic utilities for each each available
alternative for each observation.
Returns
-------
`systematic_utilities[:, None]`
... | python | def _mnl_utility_transform(systematic_utilities, *args, **kwargs):
"""
Parameters
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systematic_utilities : 1D ndarray.
Should contain the systematic utilities for each each available
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`systematic_utilities[:, None]`
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timothyb0912/pylogit | pylogit/conditional_logit.py | MNLEstimator.check_length_of_initial_values | def check_length_of_initial_values(self, init_values):
"""
Ensures that `init_values` is of the correct length. Raises a helpful
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timothyb0912/pylogit | pylogit/conditional_logit.py | MNL.fit_mle | def fit_mle(self,
init_vals,
print_res=True,
method="BFGS",
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gradient_tol=1e-06,
maxiter=1000,
ridge=None,
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just_point=False,
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init_vals,
print_res=True,
method="BFGS",
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gradient_tol=1e-06,
maxiter=1000,
ridge=None,
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timothyb0912/pylogit | pylogit/mixed_logit_calcs.py | get_normal_draws | def get_normal_draws(num_mixers,
num_draws,
num_vars,
seed=None):
"""
Parameters
----------
num_mixers : int.
Should be greater than zero. Denotes the number of observations for
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num_draws,
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seed=None):
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timothyb0912/pylogit | pylogit/mixed_logit_calcs.py | create_expanded_design_for_mixing | def create_expanded_design_for_mixing(design,
draw_list,
mixing_pos,
rows_to_mixers):
"""
Parameters
----------
design : 2D ndarray.
All elements should be ints, floats, or longs. Ea... | python | def create_expanded_design_for_mixing(design,
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rows_to_mixers):
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timothyb0912/pylogit | pylogit/mixed_logit_calcs.py | calc_choice_sequence_probs | def calc_choice_sequence_probs(prob_array,
choice_vec,
rows_to_mixers,
return_type=None):
"""
Parameters
----------
prob_array : 2D ndarray.
All elements should be ints, floats, or longs. All elements sh... | python | def calc_choice_sequence_probs(prob_array,
choice_vec,
rows_to_mixers,
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timothyb0912/pylogit | pylogit/mixed_logit_calcs.py | calc_mixed_log_likelihood | def calc_mixed_log_likelihood(params,
design_3d,
alt_IDs,
rows_to_obs,
rows_to_alts,
rows_to_mixers,
choice_vector,
... | python | def calc_mixed_log_likelihood(params,
design_3d,
alt_IDs,
rows_to_obs,
rows_to_alts,
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timothyb0912/pylogit | pylogit/mixed_logit_calcs.py | calc_mixed_logit_gradient | def calc_mixed_logit_gradient(params,
design_3d,
alt_IDs,
rows_to_obs,
rows_to_alts,
rows_to_mixers,
choice_vector,
... | python | def calc_mixed_logit_gradient(params,
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timothyb0912/pylogit | pylogit/mixed_logit_calcs.py | calc_neg_log_likelihood_and_neg_gradient | def calc_neg_log_likelihood_and_neg_gradient(beta,
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timothyb0912/pylogit | pylogit/mixed_logit_calcs.py | calc_bhhh_hessian_approximation_mixed_logit | def calc_bhhh_hessian_approximation_mixed_logit(params,
design_3d,
alt_IDs,
rows_to_obs,
rows_to_alts,
... | python | def calc_bhhh_hessian_approximation_mixed_logit(params,
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alt_IDs,
rows_to_obs,
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timothyb0912/pylogit | pylogit/bootstrap_utils.py | check_conf_percentage_validity | def check_conf_percentage_validity(conf_percentage):
"""
Ensures that `conf_percentage` is in (0, 100). Raises a helpful ValueError
if otherwise.
"""
msg = "conf_percentage MUST be a number between 0.0 and 100."
condition_1 = isinstance(conf_percentage, Number)
if not condition_1:
ra... | python | def check_conf_percentage_validity(conf_percentage):
"""
Ensures that `conf_percentage` is in (0, 100). Raises a helpful ValueError
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"""
msg = "conf_percentage MUST be a number between 0.0 and 100."
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timothyb0912/pylogit | pylogit/bootstrap_utils.py | ensure_samples_is_ndim_ndarray | def ensure_samples_is_ndim_ndarray(samples, name='bootstrap', ndim=2):
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Ensures that `samples` is an `ndim` numpy array. Raises a helpful
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"""
assert isinstance(ndim, int)
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | ensure_wide_weights_is_1D_or_2D_ndarray | def ensure_wide_weights_is_1D_or_2D_ndarray(wide_weights):
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raise ValueError(msg)
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Ensures that `wide_weights` is a 1D or 2D ndarray. Raises a helpful
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | check_validity_of_long_form_args | def check_validity_of_long_form_args(model_obj, wide_weights, rows_to_obs):
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# Ensure model_obj has the necessary method for create_long_form_weights
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"""
Ensures the args to `create_long_form_weights` have expected properties.
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | create_long_form_weights | def create_long_form_weights(model_obj, wide_weights, rows_to_obs=None):
"""
Converts an array of weights with one element per observation (wide-format)
to an array of weights with one element per observation per available
alternative (long-format).
Parameters
----------
model_obj : an inst... | python | def create_long_form_weights(model_obj, wide_weights, rows_to_obs=None):
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_finite_diff_terms_for_abc | def calc_finite_diff_terms_for_abc(model_obj,
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... | python | def calc_finite_diff_terms_for_abc(model_obj,
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_empirical_influence_abc | def calc_empirical_influence_abc(term_plus,
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_influence_arrays_for_abc | def calc_influence_arrays_for_abc(model_obj,
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_std_error_abc | def calc_std_error_abc(empirical_influence):
"""
Calculates the standard error of the MLE estimates for use in calculating
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Parameters
----------
empirical_influence : 2D ndarray.
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"""
Calculates the standard error of the MLE estimates for use in calculating
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_acceleration_abc | def calc_acceleration_abc(empirical_influence):
"""
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Parameters
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empirical_influence : 2D ndarray.
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empirical_influence : 2D ndarray.
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_bias_abc | def calc_bias_abc(second_order_influence):
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_quadratic_coef_abc | def calc_quadratic_coef_abc(model_object,
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epsilon,
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"""
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_bias_correction_abc | def calc_bias_correction_abc(acceleration, total_curvature):
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Parameters
----------
acceleration : 1D ndarray of scalars.
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"""
Calculate the bias correction constant for the approximate bootstrap
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acceleration : 1D ndarray of scalars.
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_endpoint_from_percentile_abc | def calc_endpoint_from_percentile_abc(model_obj,
init_vals,
percentile,
bias_correction,
acceleration,
std_error,
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | efron_endpoints_for_abc_confidence_interval | def efron_endpoints_for_abc_confidence_interval(conf_percentage,
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bias_correction,
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timothyb0912/pylogit | pylogit/bootstrap_abc.py | calc_abc_interval | def calc_abc_interval(model_obj,
mle_params,
init_vals,
conf_percentage,
epsilon=0.001,
**fit_kwargs):
"""
Calculate 'approximate bootstrap confidence' intervals.
Parameters
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**fit_kwargs):
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timothyb0912/pylogit | pylogit/nested_choice_calcs.py | calc_nested_probs | def calc_nested_probs(nest_coefs,
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timothyb0912/pylogit | pylogit/nested_choice_calcs.py | calc_nested_log_likelihood | def calc_nested_log_likelihood(nest_coefs,
index_coefs,
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timothyb0912/pylogit | pylogit/nested_choice_calcs.py | prep_vectors_for_gradient | def prep_vectors_for_gradient(nest_coefs,
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timothyb0912/pylogit | pylogit/nested_choice_calcs.py | naturalize_nest_coefs | def naturalize_nest_coefs(nest_coef_estimates):
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Should contain the estimated logit's
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All values should be ints, floats, or longs.
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----... | python | def naturalize_nest_coefs(nest_coef_estimates):
"""
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----------
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timothyb0912/pylogit | pylogit/nested_choice_calcs.py | calc_nested_gradient | def calc_nested_gradient(orig_nest_coefs,
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timothyb0912/pylogit | pylogit/nested_choice_calcs.py | calc_bhhh_hessian_approximation | def calc_bhhh_hessian_approximation(orig_nest_coefs,
index_coefs,
design,
choice_vec,
rows_to_obs,
rows_to_nests,
... | python | def calc_bhhh_hessian_approximation(orig_nest_coefs,
index_coefs,
design,
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timothyb0912/pylogit | pylogit/uneven_logit.py | _uneven_utility_transform | def _uneven_utility_transform(systematic_utilities,
alt_IDs,
rows_to_alts,
shape_params,
intercept_params,
intercept_ref_pos=None,
*args, **... | python | def _uneven_utility_transform(systematic_utilities,
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timothyb0912/pylogit | pylogit/uneven_logit.py | _uneven_transform_deriv_v | def _uneven_transform_deriv_v(systematic_utilities,
alt_IDs,
rows_to_alts,
shape_params,
output_array=None,
*args, **kwargs):
"""
Parameters
----------
sy... | python | def _uneven_transform_deriv_v(systematic_utilities,
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timothyb0912/pylogit | pylogit/uneven_logit.py | _uneven_transform_deriv_shape | def _uneven_transform_deriv_shape(systematic_utilities,
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timothyb0912/pylogit | pylogit/uneven_logit.py | create_calc_dh_dv | def create_calc_dh_dv(estimator):
"""
Return the function that can be used in the various gradient and hessian
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Parameters
----------
estimator : an instance of the estimation.LogitTypeEstimator class.
... | python | def create_calc_dh_dv(estimator):
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Return the function that can be used in the various gradient and hessian
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estimator : an instance of the estimation.LogitTypeEstimator class.
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timothyb0912/pylogit | pylogit/uneven_logit.py | create_calc_dh_d_shape | def create_calc_dh_d_shape(estimator):
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Return the function that can be used in the various gradient and hessian
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timothyb0912/pylogit | pylogit/uneven_logit.py | UnevenEstimator.check_length_of_initial_values | def check_length_of_initial_values(self, init_values):
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Ensures that `init_values` is of the correct length. Raises a helpful
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Parameters
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init_values : 1D ndarray.
The initial values to start the optimizatin process with.... | python | def check_length_of_initial_values(self, init_values):
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timothyb0912/pylogit | pylogit/mixed_logit.py | mnl_utility_transform | def mnl_utility_transform(sys_utility_array, *args, **kwargs):
"""
Parameters
----------
sys_utility_array : ndarray.
Should have 1D or 2D. Should have been created by the dot product of a
design matrix and an array of index coefficients.
Returns
-------
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"""
Parameters
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sys_utility_array : ndarray.
Should have 1D or 2D. Should have been created by the dot product of a
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timothyb0912/pylogit | pylogit/mixed_logit.py | check_length_of_init_values | def check_length_of_init_values(design_3d, init_values):
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Ensures that the initial values are of the correct length, given the design
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timothyb0912/pylogit | pylogit/mixed_logit.py | add_mixl_specific_results_to_estimation_res | def add_mixl_specific_results_to_estimation_res(estimator, results_dict):
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timothyb0912/pylogit | pylogit/mixed_logit.py | MixedEstimator.convenience_split_params | def convenience_split_params(self, params, return_all_types=False):
"""
Splits parameter vector into shape, intercept, and index parameters.
Parameters
----------
params : 1D ndarray.
The array of parameters being estimated or used in calculations.
return_all... | python | def convenience_split_params(self, params, return_all_types=False):
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Splits parameter vector into shape, intercept, and index parameters.
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params : 1D ndarray.
The array of parameters being estimated or used in calculations.
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timothyb0912/pylogit | pylogit/mixed_logit.py | MixedEstimator.convenience_calc_probs | def convenience_calc_probs(self, params):
"""
Calculates the probabilities of the chosen alternative, and the long
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"""
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prob_args = (betas,
... | python | def convenience_calc_probs(self, params):
"""
Calculates the probabilities of the chosen alternative, and the long
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"""
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timothyb0912/pylogit | pylogit/mixed_logit.py | MixedEstimator.convenience_calc_log_likelihood | def convenience_calc_log_likelihood(self, params):
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timothyb0912/pylogit | pylogit/mixed_logit.py | MixedEstimator.convenience_calc_gradient | def convenience_calc_gradient(self, params):
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timothyb0912/pylogit | pylogit/mixed_logit.py | MixedEstimator.convenience_calc_hessian | def convenience_calc_hessian(self, params):
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timothyb0912/pylogit | pylogit/mixed_logit.py | MixedEstimator.convenience_calc_fisher_approx | def convenience_calc_fisher_approx(self, params):
"""
Calculates the BHHH approximation of the Fisher Information Matrix for
this model / dataset. Note that this function name is INCORRECT with
regard to the actual actions performed. The Mixed Logit model uses a
placeholder for t... | python | def convenience_calc_fisher_approx(self, params):
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timothyb0912/pylogit | pylogit/mixed_logit.py | MixedLogit.fit_mle | def fit_mle(self,
init_vals,
num_draws,
seed=None,
constrained_pos=None,
print_res=True,
method="BFGS",
loss_tol=1e-06,
gradient_tol=1e-06,
maxiter=1000,
ridge=... | python | def fit_mle(self,
init_vals,
num_draws,
seed=None,
constrained_pos=None,
print_res=True,
method="BFGS",
loss_tol=1e-06,
gradient_tol=1e-06,
maxiter=1000,
ridge=... | [
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timothyb0912/pylogit | pylogit/mixed_logit.py | MixedLogit.__filter_past_mappings | def __filter_past_mappings(self,
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"""
Parameters
----------
past_mappings : dict.
All elements should be None or compressed sparse row matrices from
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timothyb0912/pylogit | pylogit/mixed_logit.py | MixedLogit.panel_predict | def panel_predict(self,
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seed=None):
"""
Parameters
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data : string or pandas dataframe.
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return_long_probs=True,
choice_col=None,
seed=None):
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timothyb0912/pylogit | pylogit/asym_logit.py | _convert_eta_to_c | def _convert_eta_to_c(eta, ref_position):
"""
Parameters
----------
eta : 1D or 2D ndarray.
The elements of the array should be this model's 'transformed' shape
parameters, i.e. the natural log of (the corresponding shape parameter
divided by the reference shape parameter). This ... | python | def _convert_eta_to_c(eta, ref_position):
"""
Parameters
----------
eta : 1D or 2D ndarray.
The elements of the array should be this model's 'transformed' shape
parameters, i.e. the natural log of (the corresponding shape parameter
divided by the reference shape parameter). This ... | [
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... | Parameters
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eta : 1D or 2D ndarray.
The elements of the array should be this model's 'transformed' shape
parameters, i.e. the natural log of (the corresponding shape parameter
divided by the reference shape parameter). This array's elements will
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timothyb0912/pylogit | pylogit/asym_logit.py | _calc_deriv_c_with_respect_to_eta | def _calc_deriv_c_with_respect_to_eta(natural_shapes,
ref_position,
output_array=None):
"""
Parameters
----------
natural_shapes : 1D ndarray.
Should have one element per available alternative in the dataset whose
... | python | def _calc_deriv_c_with_respect_to_eta(natural_shapes,
ref_position,
output_array=None):
"""
Parameters
----------
natural_shapes : 1D ndarray.
Should have one element per available alternative in the dataset whose
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natural_shapes : 1D ndarray.
Should have one element per available alternative in the dataset whose
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`ref_position` elements in it.
ref_position : int.
Specifies the position in the array of natura... | [
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timothyb0912/pylogit | pylogit/asym_logit.py | _asym_utility_transform | def _asym_utility_transform(systematic_utilities,
alt_IDs,
rows_to_alts,
eta,
intercept_params,
shape_ref_position=None,
intercept_ref_pos=None,
... | python | def _asym_utility_transform(systematic_utilities,
alt_IDs,
rows_to_alts,
eta,
intercept_params,
shape_ref_position=None,
intercept_ref_pos=None,
... | [
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systematic_utilities : 1D ndarray.
Contains the systematic utilities for each each available alternative
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alt_IDs : 1D ndarray.
All elements should be ints. There should be one row per obervation... | [
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timothyb0912/pylogit | pylogit/asym_logit.py | _asym_transform_deriv_v | def _asym_transform_deriv_v(systematic_utilities,
alt_IDs,
rows_to_alts,
eta,
ref_position=None,
output_array=None,
*args, **kwargs):
"""
Parame... | python | def _asym_transform_deriv_v(systematic_utilities,
alt_IDs,
rows_to_alts,
eta,
ref_position=None,
output_array=None,
*args, **kwargs):
"""
Parame... | [
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systematic_utilities : 1D ndarray.
Contains the systematic utilities for each each available alternative
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alt_IDs : 1D ndarray.
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