pyoptree 0.1.0

Python Implementation of Bertsimas's paper Optimal Classification Trees.

# pyoptree
Python Optimal Tree

### Dependencies
#### Python dependencies (through pip)
1. numpy
2. pandas
3. pyomo

#### Solver dependencies
The user needs to have IBM Cplex or Gurobi installed on their computer, and make sure that the executable has been added to PATH environment viariable (i.e. command `cplex` or `gurobi` can be run on terminal).

### Example
```python
import pandas as pd
from optree.optree import OptimalHyperTreeModel, OptimalTreeModel

data = pd.DataFrame({
"index": ['A', 'C', 'D', 'E', 'F'],
"x1": [1, 2, 2, 2, 3],
"x2": [1, 2, 1, 0, 1],
"y": [1, 1, -1, -1, -1]
})
test_data = pd.DataFrame({
"index": ['A', 'B', 'C', 'D', 'E', 'F', 'G'],
"x1": [1, 1, 2, 2, 2, 3, 3],
"x2": [1, 2, 2, 1, 0, 1, 0],
"y": [1, 1, 1, -1, -1, -1, -1]
})
model = OptimalHyperTreeModel(["x1", "x2"], "y", tree_depth=2, N_min=1, alpha=0.1, solver_name="cplex")
model.train(data)

print(model.predict(test_data))
```

### Todos
1. Implement "Warm Start" to speed up the time to solve the Mixed Integer Linear Programming (MILP);
2. Implement heuristics such as Generic Algorithms to approximate the optimal solution rapidly (but may be loss of accuracy);