![]() I am confused by what the error actually means. Labels_train, uniques = pd.factorize(train, sort = True)Ĭlf.fit(train, labels) # Value error occurs here ![]() Train, test = dataframe=True], dataframe=False] TestingData =, 0.77],, 30],, 0.77],, 0.77]]ĭataframe_training = pd.DataFrame(trainingData)ĭataframe_testing = pd.DataFrame(testingData)įrames = ĭataframe.rename(index = str, columns = ) ValueError: setting an array element with a sequence.įrom sklearn.ensemble import RandomForestClassifierįrom trics import confusion_matrix When I call the fit() function, I get the following error: In my code, I have 1 feature (1 column in the data table), and each entry in a column is a numpy array. In the tutorial, there are 4 features (4 columns in the data table), and each entry in a column is a number. My code follows this tutorial line by line, but the only major difference is the structure of the data. I have been using this tutorial as a guide. Trade-off curves can easily be computed in parallel.As part of a project, I am trying to use the random forest classifier from Python's SKLearn library. ylabel ( r 'x_ \gamma', fontsize = 16 ) plt. xlabel ( r '\gamma', fontsize = 16 ) plt. plot ( gamma_vals, for xi in x_values ]) plt. subplot ( 212 ) for i in range ( m ): plt. ![]() title ( 'Trade-Off Curve for LASSO', fontsize = 16 ) # Plot entries of x vs. figure ( figsize = ( 6, 10 )) # Plot trade-off curve. solve () # Use expr.value to get the numerical value of # an expression in the problem. logspace ( - 4, 6 ) for val in gamma_vals : gamma. ||x||_1 sq_penalty = l1_penalty = x_values = gamma_vals = numpy. Problem ( obj ) # Construct a trade-off curve of ||Ax-b||^2 vs. Parameter ( nonneg = True ) # Construct the problem. randn ( n ) # gamma must be nonnegative due to DCP rules. Import cvxpy as cp import numpy import matplotlib.pyplot as plt # Problem data. The constant value must have the same dimensions and attributesĪs those specified when the parameter was created. Parameters can be assigned a constant value any time after they are created. These attributes are used in Disciplined Convex Programming and are unknown unless specified. Sign of the parameter’s entries, whether the parameter is symmetric, etc. When youĬreate a parameter you have the option of specifying attributes such as the Parameters can be vectors or matrices, just like variables. ![]() This section, be sure to read the tutorial on Disciplined Parametrized Programming (DPP). Substantially faster than repeatedly solving a new problem: after reading In manyĬases, solving a parametrized program multiple times can be Of a constant in a problem without reconstructing the entire problem. The purpose of parameters is to change the value CVXPY will raise an exception if you write a chained constraint. The Python interpreter treats chained constraints in such a way that CVXPY cannot capture them. ![]() Also, you cannot chain constraints together, e.g., 0 <= x <= 1 or x = y = 2. Strict inequalities don’t make sense in a real world setting. Equality and inequality constraints are elementwise, whether they involve scalars, vectors, or matrices. Optimal value 4.14133859146 Optimal var Constraints ¶Īs shown in the example code, you can use =, = to construct constraints in CVXPY. To test if a problem was solved successfully, you would use The discussion of Choosing a solver for details.ĬVXPY provides the following constants as aliases for the different status strings: If this happens you should try using other solvers. If the solver completely fails to solve the problem, CVXPY throws a SolverError exception. The problem variables are updated as usual for the type of solutionįound (i.e., optimal, unbounded, or infeasible). Problem status indicates the lower accuracy achieved. If the solver called by CVXPY solves the problem but to a lower accuracy than desired, the Notice that for a minimization problem the optimal value is inf if Status : infeasible optimal value inf status : unbounded optimal value - inf ![]()
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