CLN: Fix small code issues

Remove unnecesssary lambdas and related

Author: bashtage

linearmodels/asset_pricing/model.py

@@ -553,7 +553,7 @@ def _moments(self, eps, betas, lam, alphas, pricing_errors):
         sigma_inv = self._sigma_inv
 
         f = self.factors.ndarray
-        nobs, nf, nport, nrf, s1, s2, s3 = self._boundaries()
+        nobs, nf, nport, _, s1, s2, s3 = self._boundaries()
         fc = np.c_[np.ones((nobs, 1)), f]
         f_rep = np.tile(fc, (1, nport))
         eps_rep = np.tile(eps, (nf + 1, 1))

linearmodels/iv/covariance.py

@@ -2,7 +2,7 @@
 Covariance estimation for 2SLS and LIML IV estimators
 """
 from numpy import (arange, argsort, asarray, ceil, cos, empty, int64, ones, pi,
-                   r_, sin, sum, unique, where, zeros)
+                   r_, sin, sum as npsum, unique, where, zeros)
 from numpy.linalg import inv, pinv
 
 CLUSTER_ERR = """
@@ -221,7 +221,7 @@ def kernel_optimal_bandwidth(x, kernel='bartlett'):
     for i in range(1, m_star + 1):
         sigma[i] = x[i:].T @ x[:-i] / t
     s0 = sigma[0] + 2 * sigma[1:].sum()
-    sq = 2 * sum(sigma[1:] * arange(1, m_star + 1) ** q)
+    sq = 2 * npsum(sigma[1:] * arange(1, m_star + 1) ** q)
     rate = 1 / (2 * q + 1)
     gamma = c * ((sq / s0) ** 2) ** rate
     m = gamma * t ** rate
@@ -314,7 +314,7 @@ def __repr__(self):
     def s(self):
         """Score covariance estimate"""
         x, z, eps = self.x, self.z, self.eps
-        nobs, nvar = x.shape
+        nobs = x.shape[0]
         s2 = eps.T @ eps / nobs
         pinvz = self._pinvz
         v = (x.T @ z) @ (pinvz @ x) / nobs
@@ -348,7 +348,7 @@ def s2(self):
         """
         Estimated variance of residuals. Small-sample adjusted if debiased.
         """
-        nobs, nvar = self.x.shape
+        nobs = self.x.shape[0]
         eps = self.eps
 
         return self._scale * eps.T @ eps / nobs

linearmodels/iv/data.py

@@ -85,9 +85,7 @@ def __init__(self, x, var_name='x', nobs=None, convert_dummies=True, drop_first=
             all_numeric = True
             for col in x:
                 c = x[col]
-                if is_string_dtype(c.dtype) and \
-                        c.map(lambda v: is_string_like(v)).all():
-
+                if is_string_dtype(c.dtype) and c.map(is_string_like).all():
                     c = c.astype('category')
                     if not copied:
                         x = x.copy()

linearmodels/iv/gmm.py

@@ -410,7 +410,7 @@ def __str__(self):
     @property
     def cov(self):
         x, z, eps, w = self.x, self.z, self.eps, self.w
-        nobs, nvar = x.shape
+        nobs = x.shape[0]
         xpz = x.T @ z / nobs
         xpzw = xpz @ w
         xpzwzpx_inv = inv(xpzw @ xpz.T)

linearmodels/iv/model.py

@@ -492,7 +492,7 @@ def _f_statistic(self, params, cov, debiased):
         return wald
 
     def _post_estimation(self, params, cov_estimator, cov_type):
-        vars = self._columns
+        columns = self._columns
         index = self._index
         eps = self.resids(params)
         y = self.dependent.pandas
@@ -512,17 +512,17 @@ def _post_estimation(self, params, cov_estimator, cov_type):
         r2 = 1 - residual_ss / total_ss
 
         fstat = self._f_statistic(params, cov, debiased)
-        out = {'params': Series(params.squeeze(), vars, name='parameter'),
+        out = {'params': Series(params.squeeze(), columns, name='parameter'),
                'eps': Series(eps.squeeze(), index=index, name='residual'),
                'weps': Series(weps.squeeze(), index=index, name='weighted residual'),
-               'cov': DataFrame(cov, columns=vars, index=vars),
+               'cov': DataFrame(cov, columns=columns, index=columns),
                's2': float(cov_estimator.s2),
                'debiased': debiased,
                'residual_ss': float(residual_ss),
                'total_ss': float(total_ss),
                'r2': float(r2),
                'fstat': fstat,
-               'vars': vars,
+               'vars': columns,
                'instruments': self._instr_columns,
                'cov_config': cov_estimator.config,
                'cov_type': cov_type,

linearmodels/iv/results.py

@@ -18,7 +18,7 @@
 
 
 def stub_concat(lists, sep='='):
-    col_size = max([max(map(lambda s: len(s), l)) for l in lists])
+    col_size = max([max(map(len, l)) for l in lists])
     out = []
     for l in lists:
         out.extend(l)
@@ -29,7 +29,7 @@ def stub_concat(lists, sep='='):
 def table_concat(lists, sep='='):
     col_sizes = []
     for l in lists:
-        size = list(map(lambda r: list(map(lambda v: len(v), r)), l))
+        size = list(map(lambda r: list(map(len, r)), l))
         col_sizes.append(list(array(size).max(0)))
     col_size = array(col_sizes).max(axis=0)
     sep_cols = [sep * (cs + 2) for cs in col_size]
@@ -644,7 +644,7 @@ def durbin(self, variables=None):
         if variables is not None:
             null = 'Variables {0} are exogenous'.format(', '.join(variables))
 
-        e0, e1, e2, nobs, nexog, nendog, ntested = self._endogeneity_setup(variables)
+        e0, e1, e2, nobs, _, _, ntested = self._endogeneity_setup(variables)
         stat = e1.T @ e1 - e2.T @ e2
         stat /= (e0.T @ e0) / nobs
 
@@ -1322,7 +1322,7 @@ def summary(self):
         for key in self._results:
             res = self._results[key]
             all_instr.append(res.model.instruments.cols)
-        ninstr = max(map(lambda l: len(l), all_instr))
+        ninstr = max(map(len, all_instr))
         instruments = []
         instrument_stub = ['Instruments']
         for i in range(ninstr):

linearmodels/panel/data.py

@@ -79,7 +79,7 @@ def to_frame(self):
 
 
 def convert_columns(s, drop_first):
-    if is_string_dtype(s.dtype) and s.map(lambda v: is_string_like(v)).all():
+    if is_string_dtype(s.dtype) and s.map(is_string_like).all():
         s = s.astype('category')
 
     if is_categorical(s):

linearmodels/panel/results.py

@@ -779,7 +779,7 @@ def summary(self):
             effects = getattr(res, 'included_effects', [])
             all_effects.append(effects)
 
-        neffect = max(map(lambda l: len(l), all_effects))
+        neffect = max(map(len, all_effects))
         effects = []
         effects_stub = ['Effects']
         for i in range(neffect):

linearmodels/system/covariance.py

@@ -227,11 +227,11 @@ def _gls_cov(self):
     def _adjustment(self):
         if not self._debiased:
             return 1.0
-        k = list(map(lambda s: s.shape[1], self._x))
+        ks = list(map(lambda s: s.shape[1], self._x))
         nobs = self._x[0].shape[0]
         adj = []
-        for i in range(len(k)):
-            adj.append(nobs / (nobs - k[i]) * ones((k[i], 1)))
+        for k in ks:
+            adj.append(nobs / (nobs - k) * ones((k, 1)))
         adj = vstack(adj)
         adj = sqrt(adj)
         return adj @ adj.T

linearmodels/system/model.py

@@ -167,7 +167,7 @@ def _parse(self):
         else:
             formula = formula.replace('\n', ' ').strip()
             parts = formula.split('}')
-            for i, part in enumerate(parts):
+            for part in parts:
                 base_key = None
                 part = part.strip()
                 if part == '':
@@ -506,7 +506,7 @@ def _validate_data(self):
                 raise ValueError('Equation {eq} instrument array is full '
                                  'rank'.format(eq=label))
 
-        for lhs, rhs, label in zip(self._y, self._x, self._eq_labels):
+        for rhs in self._x:
             const, const_loc = has_constant(rhs)
             constant.append(const)
             constant_loc.append(const_loc)