renaming directory + creating dataset directory

1 files changed, 135 insertions, 0 deletions

diff --git a/src/convex_optimization.py b/src/convex_optimization.py
new file mode 100644
index 0000000..530e3f5
--- /dev/null
+++ b/src/convex_optimization.py
@@ -0,0 +1,135 @@
+import theano
+import cascade_creation
+from theano import tensor, function
+import numpy as np
+import timeout
+import cvxopt
+
+
+@timeout.timeout(10)
+def l1obj_l2constraint(M_val, w_val):
+    """
+    Solves:
+        min - sum_j theta_j
+        s.t theta_j <= 0
+            |e^{M*theta} - (1 - w)|_2 <= 1
+    """
+    assert len(M_val) == len(w_val)
+
+    if M_val.dtype == bool:
+        M_val = M_val.astype('float32')
+
+    m, n = M_val.shape
+    c = cvxopt.matrix(-1.0, (n,1))
+
+    theta = tensor.row().T
+    z = tensor.row().T
+    theta_ = theta.flatten()
+    z_ = z.flatten()
+    M = theano.shared(M_val.astype(theano.config.floatX))
+    w = theano.shared(w_val.astype(theano.config.floatX))
+    y = (tensor.exp(M.dot(theta_)) - (1 - w)).norm(2) - 1
+    y_diff = tensor.grad(y, theta_)
+    y_hess = z[0] * theano.gradient.hessian(y, theta_)
+    f_x = theano.function([theta], [y, y_diff], allow_input_downcast=True)
+    f_xz = theano.function([theta, z], [y, y_diff, y_hess],
+                            allow_input_downcast=True)
+
+    def F(x=None, z=None):
+        if x is None:
+            return 1, cvxopt.matrix(.0001, (n,1))
+        elif z is None:
+            y, y_diff = f_x(x)
+            return cvxopt.matrix(float(y), (1, 1)),\
+                    cvxopt.matrix(y_diff.astype("float64")).T
+        else:
+            y, y_diff, y_hess = f_xz(x, z)
+            return cvxopt.matrix(float(y), (1, 1)), \
+                    cvxopt.matrix(y_diff.astype("float64")).T, \
+                    cvxopt.matrix(y_hess.astype("float64"))
+
+    G = cvxopt.spdiag([1 for i in xrange(n)])
+    h = cvxopt.matrix(0.0, (n,1))
+
+    cvxopt.solvers.options['show_progress'] = False
+    try:
+        theta = cvxopt.solvers.cpl(c,F, G, h)['x']
+    except ArithmeticError:
+        print "ArithmeticError thrown, change initial point"+\
+              " given to the solver"
+
+    return 1 - np.exp(theta), theta
+
+
+@timeout.timeout(10)
+def l1obj_l2penalization(M_val, w_val, lbda):
+    """
+    Solves:
+        min - sum_j theta_j + lbda*|e^{M*theta} - (1 - w)|_2
+        s.t theta_j <= 0
+    """
+    assert len(M_val) == len(w_val)
+
+    if M_val.dtype == bool:
+        M_val = M_val.astype('float32')
+
+    if type(lbda) == int:
+        lbda = np.array(lbda)
+
+    m, n = M_val.shape
+
+    theta = tensor.row().T
+    z = tensor.row().T
+    theta_ = theta.flatten()
+    z_ = z.flatten()
+    M = theano.shared(M_val.astype(theano.config.floatX))
+    w = theano.shared(w_val.astype(theano.config.floatX))
+    lbda = theano.shared(lbda.astype(theano.config.floatX))
+    y = (theta_).norm(1) + lbda * (
+        tensor.exp(M.dot(theta_)) - (1 - w)).norm(2)
+    y_diff = tensor.grad(y, theta_)
+    y_hess = z[0] * theano.gradient.hessian(y, theta_)
+    f_x = theano.function([theta], [y, y_diff], allow_input_downcast=True)
+    f_xz = theano.function([theta, z], [y, y_diff, y_hess],
+                           allow_input_downcast=True)
+
+    def F(x=None, z=None):
+        if x is None:
+            return 0, cvxopt.matrix(.0001, (n,1))
+        elif z is None:
+            y, y_diff = f_x(x)
+            return cvxopt.matrix(float(y), (1, 1)),\
+                   cvxopt.matrix(y_diff.astype("float64")).T
+        else:
+            y, y_diff, y_hess = f_xz(x, z)
+            return cvxopt.matrix(float(y), (1, 1)), \
+                    cvxopt.matrix(y_diff.astype("float64")).T, \
+                    cvxopt.matrix(y_hess.astype("float64"))
+
+    G = cvxopt.spdiag([1 for i in xrange(n)])
+    h = cvxopt.matrix(0.0, (n,1))
+
+    cvxopt.solvers.options['show_progress'] = False
+    try:
+        theta = cvxopt.solvers.cp(F, G, h)['x']
+    except ArithmeticError:
+        print "ArithmeticError thrown, change initial point"+\
+              " given to the solver"
+
+    return 1 - np.exp(theta), theta
+
+
+def test():
+    """
+    unit test
+    """
+    lbda = 10
+    G = cascade_creation.InfluenceGraph(max_proba=.8)
+    G.erdos_init(n=100, p = .1)
+    A = cascade_creation.generate_cascades(G, .1, 2000)
+    M_val, w_val = cascade_creation.icc_matrixvector_for_node(A, 0)
+    p_vec, theta = l1obj_l2penalization(M_val, w_val, lbda)
+    print p_vec
+
+if __name__=="__main__":
+    test()