Add minimize

datascience/tables.py

@@ -1717,13 +1717,16 @@ def scatter(self, column_for_x, select=None, overlay=True, fit_line=False, **var
             y_labels = self._as_labels(select)
 
         def draw(axis, label, color):
+            if 'color' in options:
+                color = options.pop('color')
             axis.scatter(xdata, self[label], color=color, **options)
             if fit_line:
                 m,b = np.polyfit(xdata, self[label], 1)
                 minx, maxx = np.min(xdata),np.max(xdata)
                 axis.plot([minx,maxx],[m*minx+b,m*maxx+b], color=color)
 
-        self._visualize(column_for_x, y_labels, None, overlay, draw, _vertical_x)
+        x_label = self._as_label(column_for_x)
+        self._visualize(x_label, y_labels, None, overlay, draw, _vertical_x)
 
     def _visualize(self, x_label, y_labels, ticks, overlay, draw, annotate, width=6, height=4):
         """Generic visualization that overlays or separates the draw function.

datascience/util.py

@@ -1,6 +1,7 @@
 """Utility functions"""
 
-__all__ = ['percentile', 'plot_cdf_area', 'plot_normal_cdf', 'table_apply']
+__all__ = ['percentile', 'plot_cdf_area', 'plot_normal_cdf', 'table_apply',
+           'minimize']
 
 
 import numpy as np
@@ -9,7 +10,8 @@
 matplotlib.use('agg', warn=False)
 import matplotlib.pyplot as plt
 from scipy import stats
-
+from scipy import optimize
+import functools
 
 def percentile(p, arr=None):
     """Returns the pth percentile of the input array (the value that is at
@@ -116,3 +118,15 @@ def table_apply(table, func, subset=None):
         df = pd.DataFrame(df).T
     tab = Table.from_df(df)
     return tab
+
+def minimize(f, start=None, **vargs):
+    if start is None:
+        start = [0] * f.__code__.co_argcount
+
+    @functools.wraps(f)
+    def wrapper(args):
+        return f(*args)
+
+    result = optimize.minimize(wrapper, start, **vargs)
+    return np.round(result.x, 7)
+

datascience/version.py

@@ -1 +1 @@
-__version__ = '0.5.9'
+__version__ = '0.5.10'

tests/test_util.py

@@ -40,3 +40,6 @@ def test_table_apply():
     newtab = util.table_apply(tab, lambda a: a+1, subset=['b', 'c'])
     assert all(newtab['a'] == tab['a'])
     assert all(newtab['b'] == tab['b'] + 1)
+
+def test_minimize():
+    assert [2, 1] == list(ds.minimize(lambda x, y: (x-2)**2 + (y-1)**2))