Source code for

Utility functions models code
import numpy as np
import numpy.lib.recfunctions as nprf
import pandas as pd

from statsmodels.compat.python import lzip, lmap

from import _is_using_pandas, _is_recarray
from import array_like

def asstr2(s):
    if isinstance(s, str):
        return s
    elif isinstance(s, bytes):
        return s.decode('latin1')
        return str(s)

def _make_dictnames(tmp_arr, offset=0):
    Helper function to create a dictionary mapping a column number
    to the name in tmp_arr.
    col_map = {}
    for i, col_name in enumerate(tmp_arr):
        col_map[i + offset] = col_name
    return col_map

def drop_missing(Y, X=None, axis=1):
    Returns views on the arrays Y and X where missing observations are dropped.

    Y : array_like
    X : array_like, optional
    axis : int
        Axis along which to look for missing observations.  Default is 1, ie.,
        observations in rows.

    Y : ndarray
        All Y where the
    X : ndarray

    If either Y or X is 1d, it is reshaped to be 2d.
    Y = np.asarray(Y)
    if Y.ndim == 1:
        Y = Y[:, None]
    if X is not None:
        X = np.array(X)
        if X.ndim == 1:
            X = X[:, None]
        keepidx = np.logical_and(~np.isnan(Y).any(axis),
        return Y[keepidx], X[keepidx]
        keepidx = ~np.isnan(Y).any(axis)
        return Y[keepidx]

# TODO: needs to better preserve dtype and be more flexible
# ie., if you still have a string variable in your array you do not
# want to cast it to float
# TODO: add name validator (ie., bad names for datasets.grunfeld)
[docs]def categorical(data, col=None, dictnames=False, drop=False): """ Construct a dummy matrix from categorical variables Parameters ---------- data : array_like A structured array, recarray, array, Series or DataFrame. This can be either a 1d vector of the categorical variable or a 2d array with the column specifying the categorical variable specified by the col argument. col : {str, int, None} If data is a DataFrame col must in a column of data. If data is a Series, col must be either the name of the Series or None. If data is a structured array or a recarray, `col` can be a string that is the name of the column that contains the variable. For all other arrays `col` can be an int that is the (zero-based) column index number. `col` can only be None for a 1d array. The default is None. dictnames : bool, optional If True, a dictionary mapping the column number to the categorical name is returned. Used to have information about plain arrays. drop : bool Whether or not keep the categorical variable in the returned matrix. Returns ------- dummy_matrix : array_like A matrix of dummy (indicator/binary) float variables for the categorical data. dictnames : dict[int, str], optional Mapping between column numbers and categorical names. Notes ----- This returns a dummy variable for *each* distinct variable. If a a structured or recarray is provided, the names for the new variable is the old variable name - underscore - category name. So if the a variable 'vote' had answers as 'yes' or 'no' then the returned array would have to new variables-- 'vote_yes' and 'vote_no'. There is currently no name checking. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm Univariate examples >>> import string >>> string_var = [string.ascii_lowercase[0:5], ... string.ascii_lowercase[5:10], ... string.ascii_lowercase[10:15], ... string.ascii_lowercase[15:20], ... string.ascii_lowercase[20:25]] >>> string_var *= 5 >>> string_var = np.asarray(sorted(string_var)) >>> design =, drop=True) Or for a numerical categorical variable >>> instr = np.floor(np.arange(10,60, step=2)/10) >>> design =, drop=True) With a structured array >>> num = np.random.randn(25,2) >>> struct_ar = np.zeros((25,1), ... dtype=[('var1', 'f4'),('var2', 'f4'), ... ('instrument','f4'),('str_instr','a5')]) >>> struct_ar['var1'] = num[:,0][:,None] >>> struct_ar['var2'] = num[:,1][:,None] >>> struct_ar['instrument'] = instr[:,None] >>> struct_ar['str_instr'] = string_var[:,None] >>> design =, col='instrument', drop=True) Or >>> design2 =, col='str_instr', drop=True) """ # TODO: add a NameValidator function if isinstance(col, (list, tuple)): if len(col) == 1: col = col[0] else: raise ValueError("Can only convert one column at a time") if (not isinstance(data, (pd.DataFrame, pd.Series)) and not isinstance(col, (str, int)) and col is not None): raise TypeError('col must be a str, int or None') # Pull out a Series from a DataFrame if provided if isinstance(data, pd.DataFrame): if col is None: raise TypeError('col must be a str or int when using a DataFrame') elif col not in data: raise ValueError('Column \'{0}\' not found in data'.format(col)) data = data[col] # Set col to None since we not have a Series col = None if isinstance(data, pd.Series): if col is not None and != col: raise ValueError(' does not match col ' '\'{0}\''.format(col)) data_cat = pd.Categorical(data) dummies = pd.get_dummies(data_cat) col_map = {i: cat for i, cat in enumerate(data_cat.categories) if cat in dummies} if not drop: dummies.columns = list(dummies.columns) dummies = pd.concat([dummies, data], 1) if dictnames: return dummies, col_map return dummies # catch recarrays and structured arrays elif data.dtype.names or data.__class__ is np.recarray: # deprecated: remove path after 0.12 import warnings from import recarray_warning warnings.warn(recarray_warning, FutureWarning) if not col and np.squeeze(data).ndim > 1: raise IndexError("col is None and the input array is not 1d") if isinstance(col, int): col = data.dtype.names[col] if col is None and data.dtype.names and len(data.dtype.names) == 1: col = data.dtype.names[0] tmp_arr = np.unique(data[col]) # if the cols are shape (#,) vs (#,1) need to add an axis and flip _swap = True if data[col].ndim == 1: tmp_arr = tmp_arr[:, None] _swap = False tmp_dummy = (tmp_arr == data[col]).astype(float) if _swap: tmp_dummy = np.squeeze(tmp_dummy).swapaxes(1, 0) if not tmp_arr.dtype.names: # how do we get to this code path? tmp_arr = [asstr2(item) for item in np.squeeze(tmp_arr)] elif tmp_arr.dtype.names: tmp_arr = [asstr2(item) for item in np.squeeze(tmp_arr.tolist())] # prepend the varname and underscore, if col is numeric attribute # lookup is lost for recarrays... if col is None: try: col = data.dtype.names[0] except: col = 'var' # TODO: the above needs to be made robust because there could be many # var_yes, var_no varaibles for instance. tmp_arr = [col + '_' + item for item in tmp_arr] # TODO: test this for rec and structured arrays!!! if drop is True: if len(data.dtype) <= 1: if tmp_dummy.shape[0] < tmp_dummy.shape[1]: tmp_dummy = np.squeeze(tmp_dummy).swapaxes(1, 0) dt = lzip(tmp_arr, [tmp_dummy.dtype.str]*len(tmp_arr)) # preserve array type return np.array(lmap(tuple, tmp_dummy.tolist()), dtype=dt).view(type(data)) data = nprf.drop_fields(data, col, usemask=False, asrecarray=type(data) is np.recarray) data = nprf.append_fields(data, tmp_arr, data=tmp_dummy, usemask=False, asrecarray=type(data) is np.recarray) return data # Catch array_like for an error elif not isinstance(data, np.ndarray): raise NotImplementedError("array_like objects are not supported") else: if isinstance(col, int): offset = data.shape[1] # need error catching here? tmp_arr = np.unique(data[:, col]) tmp_dummy = (tmp_arr[:, np.newaxis] == data[:, col]).astype(float) tmp_dummy = tmp_dummy.swapaxes(1, 0) if drop is True: offset -= 1 data = np.delete(data, col, axis=1).astype(float) data = np.column_stack((data, tmp_dummy)) if dictnames is True: col_map = _make_dictnames(tmp_arr, offset) return data, col_map return data elif col is None and np.squeeze(data).ndim == 1: tmp_arr = np.unique(data) tmp_dummy = (tmp_arr[:, None] == data).astype(float) tmp_dummy = tmp_dummy.swapaxes(1, 0) if drop is True: if dictnames is True: col_map = _make_dictnames(tmp_arr) return tmp_dummy, col_map return tmp_dummy else: data = np.column_stack((data, tmp_dummy)) if dictnames is True: col_map = _make_dictnames(tmp_arr, offset=1) return data, col_map return data else: raise IndexError("The index %s is not understood" % col)
# TODO: add an axis argument to this for sysreg
[docs]def add_constant(data, prepend=True, has_constant='skip'): """ Add a column of ones to an array. Parameters ---------- data : array_like A column-ordered design matrix. prepend : bool If true, the constant is in the first column. Else the constant is appended (last column). has_constant : str {'raise', 'add', 'skip'} Behavior if ``data`` already has a constant. The default will return data without adding another constant. If 'raise', will raise an error if any column has a constant value. Using 'add' will add a column of 1s if a constant column is present. Returns ------- array_like The original values with a constant (column of ones) as the first or last column. Returned value type depends on input type. Notes ----- When the input is recarray or a pandas Series or DataFrame, the added column's name is 'const'. """ if _is_using_pandas(data, None) or _is_recarray(data): if _is_recarray(data): # deprecated: remove recarray support after 0.12 import warnings from import recarray_warning warnings.warn(recarray_warning, FutureWarning) from statsmodels.tsa.tsatools import add_trend return add_trend(data, trend='c', prepend=prepend, has_constant=has_constant) # Special case for NumPy x = np.asanyarray(data) if x.ndim == 1: x = x[:, None] elif x.ndim > 2: raise ValueError('Only implemented for 2-dimensional arrays') is_nonzero_const = np.ptp(x, axis=0) == 0 is_nonzero_const &= np.all(x != 0.0, axis=0) if is_nonzero_const.any(): if has_constant == 'skip': return x elif has_constant == 'raise': raise ValueError("data already contains a constant") x = [np.ones(x.shape[0]), x] x = x if prepend else x[::-1] return np.column_stack(x)
[docs]def isestimable(c, d): """ True if (Q, P) contrast `c` is estimable for (N, P) design `d`. From an Q x P contrast matrix `C` and an N x P design matrix `D`, checks if the contrast `C` is estimable by looking at the rank of ``vstack([C,D])`` and verifying it is the same as the rank of `D`. Parameters ---------- c : array_like A contrast matrix with shape (Q, P). If 1 dimensional assume shape is (1, P). d : array_like The design matrix, (N, P). Returns ------- bool True if the contrast `c` is estimable on design `d`. Examples -------- >>> d = np.array([[1, 1, 1, 0, 0, 0], ... [0, 0, 0, 1, 1, 1], ... [1, 1, 1, 1, 1, 1]]).T >>> isestimable([1, 0, 0], d) False >>> isestimable([1, -1, 0], d) True """ c = array_like(c, 'c', maxdim=2) d = array_like(d, 'd', ndim=2) c = c[None, :] if c.ndim == 1 else c if c.shape[1] != d.shape[1]: raise ValueError('Contrast should have %d columns' % d.shape[1]) new = np.vstack([c, d]) if np.linalg.matrix_rank(new) != np.linalg.matrix_rank(d): return False return True
def pinv_extended(X, rcond=1e-15): """ Return the pinv of an array X as well as the singular values used in computation. Code adapted from numpy. """ X = np.asarray(X) X = X.conjugate() u, s, vt = np.linalg.svd(X, 0) s_orig = np.copy(s) m = u.shape[0] n = vt.shape[1] cutoff = rcond * np.maximum.reduce(s) for i in range(min(n, m)): if s[i] > cutoff: s[i] = 1./s[i] else: s[i] = 0. res =, np.multiply(s[:, np.core.newaxis], np.transpose(u))) return res, s_orig
[docs]def recipr(x): """ Reciprocal of an array with entries less than or equal to 0 set to 0. Parameters ---------- x : array_like The input array. Returns ------- ndarray The array with 0-filled reciprocals. """ x = np.asarray(x) out = np.zeros_like(x, dtype=np.float64) nans = np.isnan(x.flat) pos = ~nans pos[pos] = pos[pos] & (x.flat[pos] > 0) out.flat[pos] = 1.0 / x.flat[pos] out.flat[nans] = np.nan return out
[docs]def recipr0(x): """ Reciprocal of an array with entries less than 0 set to 0. Parameters ---------- x : array_like The input array. Returns ------- ndarray The array with 0-filled reciprocals. """ x = np.asarray(x) out = np.zeros_like(x, dtype=np.float64) nans = np.isnan(x.flat) non_zero = ~nans non_zero[non_zero] = non_zero[non_zero] & (x.flat[non_zero] != 0) out.flat[non_zero] = 1.0 / x.flat[non_zero] out.flat[nans] = np.nan return out
[docs]def clean0(matrix): """ Erase columns of zeros: can save some time in pseudoinverse. Parameters ---------- matrix : ndarray The array to clean. Returns ------- ndarray The cleaned array. """ colsum = np.add.reduce(matrix**2, 0) val = [matrix[:, i] for i in np.flatnonzero(colsum)] return np.array(np.transpose(val))
[docs]def fullrank(x, r=None): """ Return an array whose column span is the same as x. Parameters ---------- x : ndarray The array to adjust, 2d. r : int, optional The rank of x. If not provided, determined by `np.linalg.matrix_rank`. Returns ------- ndarray The array adjusted to have full rank. Notes ----- If the rank of x is known it can be specified as r -- no check is made to ensure that this really is the rank of x. """ if r is None: r = np.linalg.matrix_rank(x) v, d, u = np.linalg.svd(x, full_matrices=False) order = np.argsort(d) order = order[::-1] value = [] for i in range(r): value.append(v[:, order[i]]) return np.asarray(np.transpose(value)).astype(np.float64)
[docs]def unsqueeze(data, axis, oldshape): """ Unsqueeze a collapsed array. Parameters ---------- data : ndarray The data to unsqueeze. axis : int The axis to unsqueeze. oldshape : tuple[int] The original shape before the squeeze or reduce operation. Returns ------- ndarray The unsqueezed array. Examples -------- >>> from numpy import mean >>> from numpy.random import standard_normal >>> x = standard_normal((3,4,5)) >>> m = mean(x, axis=1) >>> m.shape (3, 5) >>> m = unsqueeze(m, 1, x.shape) >>> m.shape (3, 1, 5) >>> """ newshape = list(oldshape) newshape[axis] = 1 return data.reshape(newshape)
def nan_dot(A, B): """ Returns, right_matrix) with the convention that nan * 0 = 0 and nan * x = nan if x != 0. Parameters ---------- A, B : ndarray """ # Find out who should be nan due to nan * nonzero should_be_nan_1 =, (B != 0)) should_be_nan_2 = != 0), np.isnan(B)) should_be_nan = should_be_nan_1 + should_be_nan_2 # Multiply after setting all nan to 0 # This is what happens if there were no nan * nonzero conflicts C =, np.nan_to_num(B)) C[should_be_nan] = np.nan return C def maybe_unwrap_results(results): """ Gets raw results back from wrapped results. Can be used in plotting functions or other post-estimation type routines. """ return getattr(results, '_results', results) class Bunch(dict): """ Returns a dict-like object with keys accessible via attribute lookup. Parameters ---------- *args Arguments passed to dict constructor, tuples (key, value). **kwargs Keyword agument passed to dict constructor, key=value. """ def __init__(self, *args, **kwargs): super(Bunch, self).__init__(*args, **kwargs) self.__dict__ = self def _ensure_2d(x, ndarray=False): """ Parameters ---------- x : ndarray, Series, DataFrame or None Input to verify dimensions, and to transform as necesary ndarray : bool Flag indicating whether to always return a NumPy array. Setting False will return an pandas DataFrame when the input is a Series or a DataFrame. Returns ------- out : ndarray, DataFrame or None array or DataFrame with 2 dimensiona. One dimensional arrays are returned as nobs by 1. None is returned if x is None. names : list of str or None list containing variables names when the input is a pandas datatype. Returns None if the input is an ndarray. Notes ----- Accepts None for simplicity """ if x is None: return x is_pandas = _is_using_pandas(x, None) if x.ndim == 2: if is_pandas: return x, x.columns else: return x, None elif x.ndim > 2: raise ValueError('x mst be 1 or 2-dimensional.') name = if is_pandas else None if ndarray: return np.asarray(x)[:, None], name else: return pd.DataFrame(x), name