diff options
Diffstat (limited to 'experiments')
| -rw-r--r-- | experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o | bin | 288600 -> 256828 bytes | |||
| -rw-r--r-- | experiments/ml.pyx | 3 | ||||
| -rw-r--r-- | experiments/ml2.pyx | 24 | ||||
| -rw-r--r-- | experiments/ml3.c | 1306 | ||||
| -rw-r--r-- | experiments/ml3.pyx | 22 | ||||
| -rwxr-xr-x | experiments/ml3.so | bin | 110580 -> 97868 bytes | |||
| -rw-r--r-- | experiments/out.log | 1200 | ||||
| -rw-r--r-- | experiments/process.py | 8 |
8 files changed, 1534 insertions, 1029 deletions
diff --git a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o Binary files differindex 805ea03..445b01b 100644 --- a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o +++ b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o diff --git a/experiments/ml.pyx b/experiments/ml.pyx index 5016347..785986c 100644 --- a/experiments/ml.pyx +++ b/experiments/ml.pyx @@ -6,9 +6,6 @@ from libc.math cimport log, exp DTYPE = np.float64 ctypedef np.float_t DTYPE_t -cdef DTYPE_t plogis(DTYPE_t weight, DTYPE_t delta): - return 1./(1. + exp(-weight/delta)) - cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): """weight for successful infection, exponential time model""" diff --git a/experiments/ml2.pyx b/experiments/ml2.pyx index 95f6f34..fb2ec0d 100644 --- a/experiments/ml2.pyx +++ b/experiments/ml2.pyx @@ -6,20 +6,17 @@ from libc.math cimport log, exp DTYPE = np.float64 ctypedef np.float_t DTYPE_t -cdef DTYPE_t plogis(DTYPE_t weight, DTYPE_t delta): - return 1./(1. + exp(-weight/delta)) - cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): """weight for successful infection, exponential time model""" cdef DTYPE_t structural, temporal, result - structural = dist * log(delta) - # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta) + # structural = delta ** dist + structural = delta/(1. + exp(-w1*lmbda) + exp(-w2*lmbda) + exp(-w3*lmbda)) temporal = log(exp(alpha)-1.) - alpha*dt # temporal = 1 - exp(-alpha*dt) # if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR' # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01 - result = structural + temporal + result = log(structural) + temporal # print 'st', structural, temporal return result @@ -27,8 +24,8 @@ cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): """weight for failed infection, exponential time model""" cdef DTYPE_t structural, temporal, result - structural = delta ** dist - # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta) + # structural = delta ** dist + structural = delta/(1. + exp(-w1/lmbda) + exp(-w2/lmbda) + exp(-w3/lmbda)) temporal = exp(-alpha * dt) # temporal = 1. - 1. / (1. + dt/alpha)**0.01 result = log(1. - structural + structural * temporal) @@ -40,7 +37,7 @@ def ml2(dict root_victims, dict victims, dict non_victims, DTYPE_t age, print 'ML2' cdef: int n_roots, n_victims, roots, i, dist, dt, t, l - DTYPE_t beta, ll + DTYPE_t ll list parents, failures, successes n_roots, n_victims = len(root_victims), len(victims) cdef: @@ -80,20 +77,13 @@ def ml2(dict root_victims, dict victims, dict non_victims, DTYPE_t age, for (dist, dt, w1, w2, w3) in parents] probs_nv[i] = sum(failures) - # print successes - # print failures - # print probs - # calculate log likelihood - # probs.sort(); probs = probs[::-1] # sort probs in descending order - # cdef: - # np.ndarray[DTYPE_t] cums = probs.cumsum() ll = probs_fail.sum() # add probability that all edges to victims fail ll += probs_nv.sum() # add probability that all edges to non_victims fail ll += probs.sum() # add probability for realized edges and subtract probability these edges fail roots = n_roots - beta = 0 + beta = float('nan') # print n_nodes, n_roots, n_victims, max_i, roots print parent_dists[1:100] print parent_dts[1:100] diff --git a/experiments/ml3.c b/experiments/ml3.c index 64796d7..90842d8 100644 --- a/experiments/ml3.c +++ b/experiments/ml3.c @@ -1177,11 +1177,10 @@ int __pyx_module_is_main_ml3 = 0; /* Implementation of 'ml3' */ static PyObject *__pyx_builtin_enumerate; -static PyObject *__pyx_builtin_sum; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_RuntimeError; -static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_root_victims, PyObject *__pyx_v_victims, PyObject *__pyx_v_non_victims, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_age, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta); /* proto */ +static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_root_victims, PyObject *__pyx_v_victims, CYTHON_UNUSED PyObject *__pyx_v_non_victims, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_age, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta); /* proto */ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ static char __pyx_k_B[] = "B"; @@ -1233,14 +1232,12 @@ static char __pyx_k_arange[] = "arange"; static char __pyx_k_import[] = "__import__"; static char __pyx_k_thresh[] = "thresh"; static char __pyx_k_float64[] = "float64"; -static char __pyx_k_n_nodes[] = "n_nodes"; static char __pyx_k_n_roots[] = "n_roots"; static char __pyx_k_parents[] = "parents"; static char __pyx_k_victims[] = "victims"; static char __pyx_k_beta_add[] = "beta_add"; static char __pyx_k_failures[] = "failures"; static char __pyx_k_max_beta[] = "max_beta"; -static char __pyx_k_probs_nv[] = "probs_nv"; static char __pyx_k_enumerate[] = "enumerate"; static char __pyx_k_max_roots[] = "max_roots"; static char __pyx_k_n_victims[] = "n_victims"; @@ -1293,7 +1290,6 @@ static PyObject *__pyx_n_s_max_beta; static PyObject *__pyx_n_s_max_beta_add; static PyObject *__pyx_n_s_max_roots; static PyObject *__pyx_n_s_ml3; -static PyObject *__pyx_n_s_n_nodes; static PyObject *__pyx_n_s_n_roots; static PyObject *__pyx_n_s_n_victims; static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous; @@ -1307,7 +1303,6 @@ static PyObject *__pyx_n_s_parents; static PyObject *__pyx_n_s_prob; static PyObject *__pyx_n_s_probs; static PyObject *__pyx_n_s_probs_fail; -static PyObject *__pyx_n_s_probs_nv; static PyObject *__pyx_n_s_range; static PyObject *__pyx_n_s_root_victims; static PyObject *__pyx_n_s_roots; @@ -1323,9 +1318,9 @@ static PyObject *__pyx_n_s_w1; static PyObject *__pyx_n_s_w2; static PyObject *__pyx_n_s_w3; static PyObject *__pyx_n_s_zeros; +static PyObject *__pyx_float__1; static PyObject *__pyx_float_1_; -static PyObject *__pyx_float__05; -static PyObject *__pyx_float_0_09; +static PyObject *__pyx_float_0_1; static PyObject *__pyx_tuple_; static PyObject *__pyx_tuple__2; static PyObject *__pyx_tuple__3; @@ -1450,7 +1445,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, int __ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int __pyx_v_dist, int __pyx_v_dt, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w1, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w2, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w3) { __pyx_t_3ml3_DTYPE_t __pyx_v_structural; - __pyx_t_3ml3_DTYPE_t __pyx_v_temporal; + CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_temporal; __pyx_t_3ml3_DTYPE_t __pyx_v_result; __pyx_t_3ml3_DTYPE_t __pyx_r; __Pyx_RefNannyDeclarations @@ -1470,21 +1465,21 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int __pyx_v_dist, int __ * # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta) * temporal = exp(-alpha * dt) # <<<<<<<<<<<<<< * # temporal = 1. - 1. / (1. + dt/alpha)**0.01 - * result = log(1. - structural + structural * temporal) + * result = log(1. - structural) */ __pyx_v_temporal = exp(((-__pyx_v_alpha) * __pyx_v_dt)); /* "ml3.pyx":35 * temporal = exp(-alpha * dt) * # temporal = 1. - 1. / (1. + dt/alpha)**0.01 - * result = log(1. - structural + structural * temporal) # <<<<<<<<<<<<<< + * result = log(1. - structural) # <<<<<<<<<<<<<< * # print 'stnv', structural, temporal * return result */ - __pyx_v_result = log(((1. - __pyx_v_structural) + (__pyx_v_structural * __pyx_v_temporal))); + __pyx_v_result = log((1. - __pyx_v_structural)); /* "ml3.pyx":37 - * result = log(1. - structural + structural * temporal) + * result = log(1. - structural) * # print 'stnv', structural, temporal * return result # <<<<<<<<<<<<<< * @@ -1521,7 +1516,7 @@ static PyMethodDef __pyx_mdef_3ml3_1ml3 = {"ml3", (PyCFunction)__pyx_pw_3ml3_1ml static PyObject *__pyx_pw_3ml3_1ml3(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_root_victims = 0; PyObject *__pyx_v_victims = 0; - PyObject *__pyx_v_non_victims = 0; + CYTHON_UNUSED PyObject *__pyx_v_non_victims = 0; CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_age; __pyx_t_3ml3_DTYPE_t __pyx_v_alpha; __pyx_t_3ml3_DTYPE_t __pyx_v_delta; @@ -1620,10 +1615,9 @@ static PyObject *__pyx_pw_3ml3_1ml3(PyObject *__pyx_self, PyObject *__pyx_args, return __pyx_r; } -static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_root_victims, PyObject *__pyx_v_victims, PyObject *__pyx_v_non_victims, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_age, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta) { +static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_root_victims, PyObject *__pyx_v_victims, CYTHON_UNUSED PyObject *__pyx_v_non_victims, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_age, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta) { int __pyx_v_n_roots; int __pyx_v_n_victims; - CYTHON_UNUSED int __pyx_v_n_nodes; int __pyx_v_roots; int __pyx_v_i; int __pyx_v_dist; @@ -1639,7 +1633,6 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject PyObject *__pyx_v_successes = 0; PyArrayObject *__pyx_v_probs = 0; PyArrayObject *__pyx_v_probs_fail = 0; - PyArrayObject *__pyx_v_probs_nv = 0; PyArrayObject *__pyx_v_parent_dists = 0; PyArrayObject *__pyx_v_parent_dts = 0; PyObject *__pyx_v_dists = NULL; @@ -1659,8 +1652,6 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject __Pyx_Buffer __pyx_pybuffer_probs; __Pyx_LocalBuf_ND __pyx_pybuffernd_probs_fail; __Pyx_Buffer __pyx_pybuffer_probs_fail; - __Pyx_LocalBuf_ND __pyx_pybuffernd_probs_nv; - __Pyx_Buffer __pyx_pybuffer_probs_nv; PyObject *__pyx_r = NULL; __Pyx_RefNannyDeclarations Py_ssize_t __pyx_t_1; @@ -1673,31 +1664,29 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject PyArrayObject *__pyx_t_8 = NULL; PyArrayObject *__pyx_t_9 = NULL; PyArrayObject *__pyx_t_10 = NULL; - PyArrayObject *__pyx_t_11 = NULL; + int __pyx_t_11; int __pyx_t_12; int __pyx_t_13; - int __pyx_t_14; - Py_ssize_t __pyx_t_15; + Py_ssize_t __pyx_t_14; + PyObject *__pyx_t_15 = NULL; PyObject *__pyx_t_16 = NULL; PyObject *__pyx_t_17 = NULL; PyObject *__pyx_t_18 = NULL; PyObject *__pyx_t_19 = NULL; PyObject *__pyx_t_20 = NULL; - PyObject *__pyx_t_21 = NULL; - PyObject *(*__pyx_t_22)(PyObject *); - int __pyx_t_23; + PyObject *(*__pyx_t_21)(PyObject *); + int __pyx_t_22; + __pyx_t_3ml3_DTYPE_t __pyx_t_23; __pyx_t_3ml3_DTYPE_t __pyx_t_24; __pyx_t_3ml3_DTYPE_t __pyx_t_25; - __pyx_t_3ml3_DTYPE_t __pyx_t_26; - double __pyx_t_27; + double __pyx_t_26; + int __pyx_t_27; int __pyx_t_28; int __pyx_t_29; int __pyx_t_30; int __pyx_t_31; int __pyx_t_32; - int __pyx_t_33; - int __pyx_t_34; - PyObject *(*__pyx_t_35)(PyObject *); + PyObject *(*__pyx_t_33)(PyObject *); int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; @@ -1710,10 +1699,6 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject __pyx_pybuffer_probs_fail.refcount = 0; __pyx_pybuffernd_probs_fail.data = NULL; __pyx_pybuffernd_probs_fail.rcbuffer = &__pyx_pybuffer_probs_fail; - __pyx_pybuffer_probs_nv.pybuffer.buf = NULL; - __pyx_pybuffer_probs_nv.refcount = 0; - __pyx_pybuffernd_probs_nv.data = NULL; - __pyx_pybuffernd_probs_nv.rcbuffer = &__pyx_pybuffer_probs_nv; __pyx_pybuffer_parent_dists.pybuffer.buf = NULL; __pyx_pybuffer_parent_dists.refcount = 0; __pyx_pybuffernd_parent_dists.data = NULL; @@ -1727,8 +1712,8 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject * DTYPE_t beta, ll, beta_add, max_beta, max_beta_add * list parents, failures, successes * n_roots, n_victims = len(root_victims), len(victims) # <<<<<<<<<<<<<< - * n_nodes = 148152 * cdef: + * np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE) */ if (unlikely(__pyx_v_root_victims == Py_None)) { PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); @@ -1743,52 +1728,43 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject __pyx_v_n_roots = __pyx_t_1; __pyx_v_n_victims = __pyx_t_2; - /* "ml3.pyx":46 - * list parents, failures, successes + /* "ml3.pyx":47 * n_roots, n_victims = len(root_victims), len(victims) - * n_nodes = 148152 # <<<<<<<<<<<<<< - * cdef: - * np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE) - */ - __pyx_v_n_nodes = 148152; - - /* "ml3.pyx":48 - * n_nodes = 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__Pyx_INCREF(__pyx_t_16); __Pyx_INCREF(__pyx_t_17); __Pyx_INCREF(__pyx_t_18); __Pyx_INCREF(__pyx_t_19); - __Pyx_INCREF(__pyx_t_20); #else { Py_ssize_t i; - PyObject** temps[5] = {&__pyx_t_16,&__pyx_t_17,&__pyx_t_18,&__pyx_t_19,&__pyx_t_20}; + PyObject** temps[5] = {&__pyx_t_15,&__pyx_t_16,&__pyx_t_17,&__pyx_t_18,&__pyx_t_19}; for (i=0; i < 5; i++) { - PyObject* item = PySequence_ITEM(sequence, i); if (unlikely(!item)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 60; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + PyObject* item = PySequence_ITEM(sequence, i); if (unlikely(!item)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 58; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(item); *(temps[i]) = item; } } #endif - __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; } else { Py_ssize_t index = -1; - PyObject** temps[5] = {&__pyx_t_16,&__pyx_t_17,&__pyx_t_18,&__pyx_t_19,&__pyx_t_20}; - __pyx_t_21 = PyObject_GetIter(__pyx_t_3); if (unlikely(!__pyx_t_21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 60; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_21); - __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_22 = Py_TYPE(__pyx_t_21)->tp_iternext; + PyObject** temps[5] = {&__pyx_t_15,&__pyx_t_16,&__pyx_t_17,&__pyx_t_18,&__pyx_t_19}; + __pyx_t_20 = PyObject_GetIter(__pyx_t_4); if (unlikely(!__pyx_t_20)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 58; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_20); + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + __pyx_t_21 = Py_TYPE(__pyx_t_20)->tp_iternext; for (index=0; index < 5; index++) { - PyObject* item = __pyx_t_22(__pyx_t_21); if (unlikely(!item)) goto __pyx_L7_unpacking_failed; + PyObject* item = __pyx_t_21(__pyx_t_20); if (unlikely(!item)) goto __pyx_L7_unpacking_failed; __Pyx_GOTREF(item); *(temps[index]) = item; } - if (__Pyx_IternextUnpackEndCheck(__pyx_t_22(__pyx_t_21), 5) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 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__LINE__; goto __pyx_L1_error;} + __pyx_t_13 = __Pyx_PyInt_As_int(__pyx_t_15); if (unlikely((__pyx_t_13 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 58; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_15); __pyx_t_15 = 0; + __pyx_t_22 = __Pyx_PyInt_As_int(__pyx_t_16); if (unlikely((__pyx_t_22 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 58; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_16); __pyx_t_16 = 0; - __pyx_t_23 = __Pyx_PyInt_As_int(__pyx_t_17); if (unlikely((__pyx_t_23 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 60; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_17); __pyx_t_17 = 0; - __pyx_v_dist = __pyx_t_14; - __pyx_v_dt = __pyx_t_23; - __Pyx_XDECREF_SET(__pyx_v_w1, __pyx_t_18); + __pyx_v_dist = __pyx_t_13; + __pyx_v_dt = __pyx_t_22; + __Pyx_XDECREF_SET(__pyx_v_w1, __pyx_t_17); + __pyx_t_17 = 0; + __Pyx_XDECREF_SET(__pyx_v_w2, __pyx_t_18); __pyx_t_18 = 0; - __Pyx_XDECREF_SET(__pyx_v_w2, __pyx_t_19); + __Pyx_XDECREF_SET(__pyx_v_w3, __pyx_t_19); __pyx_t_19 = 0; - __Pyx_XDECREF_SET(__pyx_v_w3, __pyx_t_20); - __pyx_t_20 = 0; - /* "ml3.pyx":59 + /* "ml3.pyx":57 * # fail to infect it, also computes the probability that its most * # likely parent infects it * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<< * for (dist, dt, w1, w2, w3) in parents] * # probs_fail[i] = sum(failures) */ - __pyx_t_24 = __pyx_PyFloat_AsDouble(__pyx_v_w1); if (unlikely((__pyx_t_24 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 59; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_t_25 = __pyx_PyFloat_AsDouble(__pyx_v_w2); if (unlikely((__pyx_t_25 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 59; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_t_26 = __pyx_PyFloat_AsDouble(__pyx_v_w3); 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PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 57; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_25 = __pyx_PyFloat_AsDouble(__pyx_v_w3); if (unlikely((__pyx_t_25 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 57; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyFloat_FromDouble(__pyx_f_3ml3_weight_failure(__pyx_v_dist, __pyx_v_dt, __pyx_v_alpha, __pyx_v_delta, __pyx_t_23, __pyx_t_24, __pyx_t_25)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 57; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + if (unlikely(__Pyx_ListComp_Append(__pyx_t_3, (PyObject*)__pyx_t_4))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 57; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - /* "ml3.pyx":60 + /* "ml3.pyx":58 * # likely parent infects it * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) * for (dist, dt, w1, w2, 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if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 63; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyList_GET_ITEM(__pyx_t_5, __pyx_t_14); __Pyx_INCREF(__pyx_t_4); __pyx_t_14++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 61; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #else - __pyx_t_3 = PySequence_ITEM(__pyx_t_5, __pyx_t_15); __pyx_t_15++; if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 63; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PySequence_ITEM(__pyx_t_5, __pyx_t_14); __pyx_t_14++; if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 61; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #endif - if ((likely(PyTuple_CheckExact(__pyx_t_3))) || (PyList_CheckExact(__pyx_t_3))) { - PyObject* sequence = __pyx_t_3; + if ((likely(PyTuple_CheckExact(__pyx_t_4))) || (PyList_CheckExact(__pyx_t_4))) { + PyObject* sequence = __pyx_t_4; #if CYTHON_COMPILING_IN_CPYTHON Py_ssize_t 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b/experiments/ml3.pyx @@ -6,9 +6,6 @@ from libc.math cimport log, exp DTYPE = np.float64 ctypedef np.float_t DTYPE_t -cdef DTYPE_t plogis(DTYPE_t weight, DTYPE_t delta): - return 1./(1. + exp(-weight/delta)) - cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): """weight for successful infection, exponential time model""" @@ -32,22 +29,20 @@ cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta) temporal = exp(-alpha * dt) # temporal = 1. - 1. / (1. + dt/alpha)**0.01 - result = log(1. - structural + structural * temporal) + result = log(1. - structural) # print 'stnv', structural, temporal return result def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age, DTYPE_t alpha, DTYPE_t delta): cdef: - int n_roots, n_victims, n_nodes, roots, i, dist, dt, t, l + int n_roots, n_victims, roots, i, dist, dt, t, l DTYPE_t beta, ll, beta_add, max_beta, max_beta_add list parents, failures, successes n_roots, n_victims = len(root_victims), len(victims) - n_nodes = 148152 cdef: np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE) np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE) - np.ndarray[DTYPE_t] probs_nv = np.zeros(len(non_victims), dtype=DTYPE) np.ndarray[DTYPE_t] parent_dists = np.zeros(n_victims, dtype=DTYPE) np.ndarray[DTYPE_t] parent_dts = np.zeros(n_victims, dtype=DTYPE) @@ -74,29 +69,18 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age, parent_dts[i] = dts[l] probs_fail[i] = failures[l] - # loop through non-victims - # for i, parents in enumerate(non_victims.itervalues()): - # # for each non victim node, compute the probability that all its - # # parents fail to infect it - # failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) - # for (dist, dt, w1, w2, w3) in parents] - # probs_nv[i] = sum(failures) - # print successes # print failures # print probs # calculate log likelihood - # probs.sort(); probs = probs[::-1] # sort probs in descending order - # cdef: - # np.ndarray[DTYPE_t] cums = probs.cumsum() ll = probs_fail.sum() # add probability that all edges to victims fail # ll += probs_nv.sum() # add probability that all edges to non_victims fail # print 'probs', probs max_beta_add = float('-inf') # iterate over all victim nodes to find the optimal threshold - for beta in np.arange(0.09, 1., .05): + for beta in np.arange(0.1, 1., .1): thresh = log(beta/(3012.*(1.-beta))) # print 'beta:', beta, 'thresh:', thresh, 'infected:', len(probs[probs>=thresh]) roots = n_roots + len(probs[probs<thresh]) diff --git a/experiments/ml3.so b/experiments/ml3.so Binary files differindex 853ea01..3f515d5 100755 --- a/experiments/ml3.so +++ b/experiments/ml3.so diff --git a/experiments/out.log b/experiments/out.log index b155f87..77e6ca1 100644 --- a/experiments/out.log +++ b/experiments/out.log @@ -1,200 +1,1000 @@ -0.0001 0.0001 0.99 11270 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+0.491 0.91 0.8 7329 -31999.1061224 +0.491 0.96 0.8 7053 -38252.0335274 diff --git a/experiments/process.py b/experiments/process.py index 40bc9f3..a538545 100644 --- a/experiments/process.py +++ b/experiments/process.py @@ -13,10 +13,10 @@ if __name__ == "__main__": sys.exit("usage: {0} <file>".format(sys.argv[0])) root_victims, victims, non_victims, age = load(open(sys.argv[1])) - # alphas = np.arange(5e-4, 3e-3, 1e-4) # parameter of the time component - alphas = np.logspace(-4,2,num=20) - # deltas = np.arange(0.03, 0.1, 0.005) # parameter of the structural component - deltas = np.logspace(-4,-1,num=10) + alphas = np.arange(1e-3, 5e-1, 1e-2) # parameter of the time component + # alphas = np.logspace(-4,0,num=20) + deltas = np.arange(0.01, 0.99, 0.05) # parameter of the structural component + # deltas = np.logspace(-3,-0.001,num=20) with open("out.log", "w") as fh: for alpha, delta in product(alphas, deltas): beta, roots, ll = ml3(root_victims, victims, non_victims, age, alpha, delta) 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