+- Python Forum (https://python-forum.io)
+-- Forum: Python Coding (https://python-forum.io/forum-7.html)
+--- Forum: General Coding Help (https://python-forum.io/forum-8.html)
+--- Thread: x and y must have same first dimension, but have shapes (1,) and (50,) (/thread-39162.html)
x and y must have same first dimension, but have shapes (1,) and (50,) - asja2010 - Jan-11-2023
Hey there python community, after successfully plotting a graph, when it comes to plotting the frontier of it, I get this message:
ValueError: x and y must have same first dimension, but have shapes (1,) and (50,)This is the graph:
plt.figure(figsize=(22,7))
plt.scatter(expectedVolatility,expectedReturn,c=sharpeRatio)
plt.xlabel('expected volatility')
plt.ylabel('expected log returns')
plt.colorbar(label='sharpe ratio')
plt.scatter(expectedVolatility[maxIndex],expectedReturn[maxIndex],c='red')
plt.plot(volatility_opt,returns, '--')
plt.show()I know this kind of error is very common and in my humble opinion probably it arises from here:returns = np.linspace(0, 1.50, num= 50, endpoint=True, retstep=False, dtype=None, axis=0) volatility_opt = []and the output is
volatility_opt [10.194341397342678]as the volatility_opt shouldn't be obviously just one value. I can't find yet precisely the origin, where could it be?
RE: x and y must have same first dimension, but have shapes (1,) and (50,) - deanhystad - Jan-11-2023
Include entire error message, including stack trace. If you post code, please post the code that is related to the issue. You say that volatility_opt should not be just one value, but you don't include any code showing how volatility opt is generated.
RE: x and y must have same first dimension, but have shapes (1,) and (50,) - asja2010 - Jan-12-2023
The error message is the following:
ValueError Traceback (most recent call last)
<ipython-input-182-1237a1515f04> in <module>
5 plt.colorbar(label='sharpe ratio')
6 plt.scatter(expectedVolatility[maxIndex],expectedReturn[maxIndex],c='red')
----> 7 plt.plot(volatility_opt,returns, '--')
8 plt.show()
C:\Anaconda3\lib\site-packages\matplotlib\pyplot.py in plot(scalex, scaley, data, *args, **kwargs)
2838 @_copy_docstring_and_deprecators(Axes.plot)
2839 def plot(*args, scalex=True, scaley=True, data=None, **kwargs):
-> 2840 return gca().plot(
2841 *args, scalex=scalex, scaley=scaley,
2842 **({"data": data} if data is not None else {}), **kwargs)
C:\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py in plot(self, scalex, scaley, data, *args, **kwargs)
1741 """
1742 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1743 lines = [*self._get_lines(*args, data=data, **kwargs)]
1744 for line in lines:
1745 self.add_line(line)
C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py in __call__(self, data, *args, **kwargs)
271 this += args[0],
272 args = args[1:]
--> 273 yield from self._plot_args(this, kwargs)
274
275 def get_next_color(self):
C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py in _plot_args(self, tup, kwargs)
397
398 if x.shape[0] != y.shape[0]:
--> 399 raise ValueError(f"x and y must have same first dimension, but "
400 f"have shapes {x.shape} and {y.shape}")
401 if x.ndim > 2 or y.ndim > 2:
ValueError: x and y must have same first dimension, but have shapes (1,) and (50,)For what concerns volatility_opt:def negativeSR(w):
w = np.array(w)
R = np.sum(meanlogReturns*w)
V = np.sqrt(np.dot(w.T, np.dot(Sigma,w)))
SR = R/V
return -1*SRdef b(w):
return np.sum(w)-1
w0 = [0.25, 0.25, 0.25, 0.25]
bounds = ((0,1), (0,1), (0,1), (0,1))
constraints = ({'type': 'eq', 'fun': b})
w_opt = minimize(negativeSR, w0, method='SLSQP', bounds=bounds, constraints=constraints)
w_optreturns = np.linspace(0, 1.50, num= 50, endpoint=True, retstep=False, dtype=None, axis=0)
volatility_opt = []
def GR(w):
w = np.array(w)
R = np.sum(meanlogReturns*w)
return R
def minimizeMyvolatility(w):
w =np.array(w)
V = np.sqrt(np.dot(w.T, np.dot(Sigma, w)))
return V
for R in returns:
constraints = ({'type': 'eq', 'fun': b},
{'type': 'eq', 'fun': lambda w: GR(w) - R})
opt = minimize(minimizeMyvolatility, w0, method = 'SLSQP', bounds = bounds, constraints = constraints)
volatility_opt.append(opt['fun'])with b as :def b(w):
return np.sum(w)-1and Sigma and w as :weight = np.zeros((a, 4))
expectedReturn = np.zeros(a)
expectedVolatility = np.zeros(a)
sharpeRatio = np.zeros(a)
meanlogReturns = logReturns.mean()
Sigma = logReturns.cov()
for k in range(a):
w = np.array(np.random.random(4))
w = w / np.sum(w)
weight[k,:] = wReturns of 4 tickers from yahoo lib with their closing price from 4/01/22 to 4/01/22 (or 01/4/22 to 10/4/22). LogReturns is just the log function of returns multiplied by 250 days. In fact, till the Sharpe Ratio and the frontier's plot everything was running smoothly.
RE: x and y must have same first dimension, but have shapes (1,) and (50,) - deanhystad - Jan-12-2023
An indentation error?
for R in returns:
constraints = ({'type': 'eq', 'fun': b},
{'type': 'eq', 'fun': lambda w: GR(w) - R})
opt = minimize(minimizeMyvolatility, w0, method = 'SLSQP', bounds = bounds, constraints = constraints)
--->volatility_opt.append(opt['fun'])
RE: x and y must have same first dimension, but have shapes (1,) and (50,) - asja2010 - Jan-12-2023
(Jan-12-2023, 03:46 PM)deanhystad Wrote: An indentation error?
for R in returns: constraints = ({'type': 'eq', 'fun': b}, {'type': 'eq', 'fun': lambda w: GR(w) - R}) opt = minimize(minimizeMyvolatility, w0, method = 'SLSQP', bounds = bounds, constraints = constraints) --->volatility_opt.append(opt['fun'])
Yes, that's it, now it works! I've triple-checked everything and as a pure rookie, I haven't considered the indentation...
Thank you very much for real, mr. Deanhystad :)
RE: x and y must have same first dimension, but have shapes (1,) and (50,) - deanhystad - Jan-12-2023
In Python, indentation is code.