# Database

The database management module.

## biogeme.database module

Implementation of the class Database, wrapping a pandas dataframe for specific services to Biogeme

author

Michel Bierlaire

date

Tue Mar 26 16:42:54 2019

class biogeme.database.Database(name, pandasDatabase)[source]

Bases: object

Class that contains and prepare the database.

DefineVariable(name, expression)[source]

Insert a new column in the database and define it as a variable.

__init__(name, pandasDatabase)[source]

Constructor

Parameters
• name (string) – name of the database.

• pandasDatabase (pandas.DataFrame) – data stored in a pandas data frame.

Raises

biogemeError – if the audit function detects errors.

Add a new column in the database, calculated from an expression.

Parameters
Returns

Return type

numpy.Series

Raises

ValueError – if the column name already exists.

buildPanelMap()[source]

Sorts the data so that the observations for each individuals are contiguous, and builds a map that identifies the range of indices of the observations of each individuals.

checkAvailabilityOfChosenAlt(avail, choice)[source]

Check if the chosen alternative is available for each entry in the database.

Parameters
• avail (list of biogeme.expressions.Expression) – list of expressions to evaluate the availability conditions for each alternative.

• choice (biogeme.expressions.Expression) – expression for the chosen alternative.

Returns

numpy series of bool, long as the number of entries in the database, containing True is the chosen alternative is available, False otherwise.

Return type

numpy.Series

Raises

biogemeError – if the chosen alternative does not appear in the availability dict

choiceAvailabilityStatistics(avail, choice)[source]

Calculates the number of time an alternative is chosen and available

Parameters
• avail (list of biogeme.expressions.Expression) – list of expressions to evaluate the availability conditions for each alternative.

• choice (biogeme.expressions.Expression) – expression for the chosen alternative.

Returns

for each alternative, a tuple containing the number of time it is chosen, and the number of time it is available.

Return type

dict(int: (int, int))

count(columnName, value)[source]

Counts the number of observations that have a specific value in a given column.

Parameters
• columnName (string) – name of the column.

• value (float) – value that is seeked.

Returns

Number of times that the value appears in the column.

Return type

int

data

Pandas data frame containing the data.

descriptionOfNativeDraws()[source]

Describe the draws available draws with Biogeme

Returns

dict, where the keys are the names of the draws, and the value their description

Example of output:

{'UNIFORM: Uniform U[0, 1]',
'UNIFORM_ANTI: Antithetic uniform U[0, 1]'],
'NORMAL: Normal N(0, 1) draws'}

Return type

dict

dumpOnFile()[source]

Dumps the database in a CSV formatted file.

Returns

name of the file

Return type

string

excludedData

Number of observations removed by the function biogeme.Database.remove()

fullData

Pandas data frame containing the full data. Useful when batches of the sample are used for approximating the log likelihood.

fullIndividualMap

complete map identifying the range of observations for each individual in a panel data context. None if data is not panel. Useful when batches of the sample are used to approximate the log likelihood function.

generateDraws(types, names, numberOfDraws)[source]

Generate draws for each variable.

Parameters
• types (dict) –

A dict indexed by the names of the variables, describing the types of draws. Each of them can be a native type or any type defined by the function setRandomNumberGenerators().

Native types:

• 'UNIFORM': Uniform U[0, 1],

• 'UNIFORM_ANTI: Antithetic uniform U[0, 1]’,

• 'UNIFORM_HALTON2': Halton draws with base 2, skipping the first 10,

• 'UNIFORM_HALTON3': Halton draws with base 3, skipping the first 10,

• 'UNIFORM_HALTON5': Halton draws with base 5, skipping the first 10,

• 'UNIFORM_MLHS': Modified Latin Hypercube Sampling on [0, 1],

• 'UNIFORM_MLHS_ANTI': Antithetic Modified Latin Hypercube Sampling on [0, 1],

• 'UNIFORMSYM': Uniform U[-1, 1],

• 'UNIFORMSYM_ANTI': Antithetic uniform U[-1, 1],

• 'UNIFORMSYM_HALTON2': Halton draws on [-1, 1] with base 2, skipping the first 10,

• 'UNIFORMSYM_HALTON3': Halton draws on [-1, 1] with base 3, skipping the first 10,

• 'UNIFORMSYM_HALTON5': Halton draws on [-1, 1] with base 5, skipping the first 10,

• 'UNIFORMSYM_MLHS': Modified Latin Hypercube Sampling on [-1, 1],

• 'UNIFORMSYM_MLHS_ANTI': Antithetic Modified Latin Hypercube Sampling on [-1, 1],

• 'NORMAL': Normal N(0, 1) draws,

• 'NORMAL_ANTI': Antithetic normal draws,

• 'NORMAL_HALTON2': Normal draws from Halton base 2 sequence,

• 'NORMAL_HALTON3': Normal draws from Halton base 3 sequence,

• 'NORMAL_HALTON5': Normal draws from Halton base 5 sequence,

• 'NORMAL_MLHS': Normal draws from Modified Latin Hypercube Sampling,

• 'NORMAL_MLHS_ANTI': Antithetic normal draws from Modified Latin Hypercube Sampling]

For an updated description of the native types, call the function descriptionOfNativeDraws().

• names (list of strings) – the list of names of the variables that require draws to be generated.

• numberOfDraws (int) – number of draws to generate.

Returns

a 3-dimensional table with draws. The 3 dimensions are

1. number of individuals

2. number of draws

3. number of variables

Return type

numpy.array

Example:

types = {'randomDraws1': 'NORMAL_MLHS_ANTI',
'randomDraws2': 'UNIFORM_MLHS_ANTI',
'randomDraws3': 'UNIFORMSYM_MLHS_ANTI'}
theDrawsTable = myData.generateDraws(types,
['randomDraws1', 'randomDraws2', 'randomDraws3'], 10)

Raises
• biogemeError – if a type of draw is unknown.

• biogemeError – if the output of the draw generator does not have the requested dimensions.

generateFlatPanelDataframe(saveOnFile=None, identical_columns=[])[source]

Generate a flat version of the panel data

Parameters
• saveOnFile (bool) – if True, the flat database is saved on file.

• identical_columns (list(str)) – list of columns that contain the same values for all observations of the same individual. If None, these columns are automatically detected. Default: empty list.

Returns

the flatten database, in Pandas format

Return type

pandas.DataFrame

Raises

biogemeError – if the database in not panel

getNumberOfObservations()[source]

Reports the number of observations in the database.

Note that it returns the same value, irrespectively if the database contains panel data or not.

Returns

Number of observations.

Return type

int

getSampleSize()[source]

Reports the size of the sample.

If the data is cross-sectional, it is the number of observations in the database. If the data is panel, it is the number of individuals.

Returns

Sample size.

Return type

int

individualMap

map identifying the range of observations for each individual in a panel data context. None if data is not panel.

isPanel()[source]

Tells if the data is panel or not.

Returns

True if the data is panel.

Return type

bool

name

Name of the database. Used mainly for the file name when dumping data.

numberOfDraws

Number of draws generated by the function Database.generateDraws. Value 0 if this function is not called.

panel(columnName)[source]

Defines the data as panel data

Parameters

columnName (string) – name of the columns that identifies individuals.

Raises

biogemeError – if the data are not sorted properly, that is if the data for the one individuals are not consecutive.

panelColumn

Name of the column identifying the individuals in a panel data context. None if data is not panel.

remove(expression)[source]

Removes from the database all entries such that the value of the expression is not 0.

Parameters

expression (biogeme.expressions.Expression) – expression to evaluate

sampleIndividualMapWithReplacement(size=None)[source]

Extract a random sample of the individual map from a panel data database, with replacement.

Useful for bootstrapping.

Parameters

size (int) – size of the sample. If None, a sample of the same size as the database will be generated. Default: None.

Returns

pandas dataframe with the sample.

Return type

pandas.DataFrame

Raises

biogemeError – if the database in not in panel mode.

sampleWithReplacement(size=None)[source]

Extract a random sample from the database, with replacement.

Useful for bootstrapping.

Parameters

size (int) – size of the sample. If None, a sample of the same size as the database will be generated. Default: None.

Returns

pandas dataframe with the sample.

Return type

pandas.DataFrame

sampleWithoutReplacement(samplingRate, columnWithSamplingWeights=None)[source]

Replace the data set by a sample for stochastic algorithms

Parameters
• samplingRate (float) – the proportion of data to include in the sample.

• columnWithSamplingWeights (string) – name of the column with the sampling weights. If None, each row has equal probability.

Raises

biogemeError – if the structure of the database has been modified since last sample.

scaleColumn(column, scale)[source]

Multiply an entire column by a scale value

Parameters
• column (string) – name of the column

• scale (float) – value of the scale. All values of the column will be multiplied by that scale.

setRandomNumberGenerators(rng)[source]

Defines user-defined random numbers generators.

Parameters

rng (dict) – a dictionary of generators. The keys of the dictionary characterize the name of the generators, and must be different from the pre-defined generators in Biogeme (see generateDraws() for the list). The elements of the dictionary are functions that take two arguments: the number of series to generate (typically, the size of the database), and the number of draws per series.

Example:

def logNormalDraws(sampleSize, numberOfDraws):
return np.exp(np.random.randn(sampleSize, numberOfDraws))

def exponentialDraws(sampleSize, numberOfDraws):
return -1.0 * np.log(np.random.rand(sampleSize, numberOfDraws))

# We associate these functions with a name
dict = {'LOGNORMAL':(logNormalDraws,
'Draws from lognormal distribution'),
'EXP':(exponentialDraws,
'Draws from exponential distributions')}
myData.setRandomNumberGenerators(dict)

Raises

ValueError – if a reserved keyword is used for a user-defined draws.

split(slices, groups=None)[source]

Prepare estimation and validation sets for validation.

Parameters
• slices (int) – number of slices

• groups (str) – name of the column that defines the ID of the groups. Data belonging to the same groups will be maintained together.

Returns

list of estimation and validation data sets

Return type

list(tuple(pandas.DataFrame, pandas.DataFrame))

Raises

biogemeError – if the number of slices is less than two

suggestScaling(columns=None, reportAll=False)[source]

Suggest a scaling of the variables in the database.

For each column, $$\delta$$ is the difference between the largest and the smallest value, or one if the difference is smaller than one. The level of magnitude is evaluated as a power of 10. The suggested scale is the inverse of this value.

$s = \frac{1}{10^{|\log_{10} \delta|}}$

where $$|x|$$ is the integer closest to $$x$$.

Parameters
• columns (list(str)) – list of columns to be considered. If None, all of them will be considered.

• reportAll (bool) – if False, remove entries where the suggested scale is 1, 0.1 or 10

Returns

A Pandas dataframe where each row contains the name of the variable and the suggested scale s. Ideally, the column should be multiplied by s.

Return type

pandas.DataFrame

Raises

biogemeError – if a variable in columns is unknown.

sumFromDatabase(expression)[source]

Calculates the value of an expression for each entry in the database, and returns the sum.

Obsolete since 3.2.9.

Parameters

expression (biogeme.expressions.Expression) – expression to evaluate

Returns

sum of the expressions over the database.

Return type

float

Raises

biogemeError – if called.

theDraws

Draws for Monte-Carlo integration

typesOfDraws

Types of draws for Monte Carlo integration

useFullSample()[source]

Re-establish the full sample for calculation of the likelihood

userRandomNumberGenerators

Dictionary containing user defined random number generators. Defined by the function Database.setRandomNumberGenerators that checks that reserved keywords are not used. The element of the dictionary is a tuple with two elements: (0) the function generating the draws, and (1) a string describing the type of draws

valuesFromDatabase(expression)[source]

Evaluates an expression for each entry of the database.

Parameters

expression (biogeme.expressions.Expression.) – expression to evaluate

Returns

numpy series, long as the number of entries in the database, containing the calculated quantities.

Return type

numpy.Series

variables

names of the headers of the database so that they can be used as an object of type biogeme.expressions.Expression. Initialized by _generateHeaders()

class biogeme.database.EstimationValidation(estimation, validation)

Bases: tuple

estimation

Alias for field number 0

validation

Alias for field number 1

biogeme.database.logger = <biogeme.messaging.bioMessage object>

Logger that controls the output of messages to the screen and log file. Type: class biogeme.messaging.bioMessage.