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How to use get_free method in avocado

Best Python code snippet using avocado_python

models.py

Source:models.py

...35    self.L2Lambda = None36  def eval_objective(self, free_params):37    pass38  def get_params(self):39    return self.params.get_free()40  def dump_state(self, xk):41    '''42    callback to save the state to disk during optimization43    '''44    filename = 'state.txt'45    if not os.path.exists(filename):46      past = np.zeros((0,xk.shape[0]))47    else:48      past = np.loadtxt(filename)49      if past.ndim < 2:50        past = past.reshape(1,-1)51    np.savetxt(filename, np.append(past, xk.reshape(1,-1), axis=0))52  def fit(self,53          warm_start=True,54          label=None,55          save=False, 56          use_fit_L1=False,57          tol=1e-15,58          dump_state=False,59          **kwargs):60    '''61    Trains the model62    '''63    if False and hasattr(self, 'objective_gradient'):64      eval_objective_grad = self.objective_gradient65    else:66      eval_objective_grad = autograd.grad(self.eval_objective)67      68    eval_objective = lambda theta: self.eval_objective(theta)69    eval_objective_hess = autograd.hessian(self.eval_objective)70    eval_objective_hvp = autograd.hessian_vector_product(self.eval_objective)71    if use_fit_L1:72      fitL1.fit_L1(self, **kwargs)73    else:74      if dump_state:75        callback = self.dump_state76      else:77        callback = None78      opt_res = scipy.optimize.minimize(eval_objective,79                                      jac=eval_objective_grad,80                                      hessp=eval_objective_hvp,81                                      hess=eval_objective_hess,82                                      x0=copy.deepcopy(self.params.get_free()),83                                      callback=callback,84                                      method='trust-ncg',85                                      tol=tol,86                                      options={87                                        'initial_trust_radius':0.1,88                                        'max_trust_radius':1,89                                        'gtol':tol,90                                        'disp':False,91                                        'maxiter':10000092                                      })93      self.params.set_free(opt_res.x)94      if np.linalg.norm(opt_res.jac) > .01:95        print('Got grad norm', np.linalg.norm(opt_res.jac))96  # TODO: can we rewrite to avoid rewriting the instance var each time?97  def weighted_model_objective(self, example_weights, free_params):98    ''' The actual objective that we differentiate '''99    self.example_weights = example_weights100    return self.eval_objective(free_params)101  def compute_gradients(self, weights):102    if self.is_a_glm:103      self.compute_derivs()104      grads = (self.D1 * weights)[np.newaxis,:] * self.training_data.X.copy().T105    else:106      dObj_dParams = autograd.jacobian(self.weighted_model_objective, argnum=1)107      d2Obj_dParamsdWeights = autograd.jacobian(dObj_dParams, argnum=0)108      array_box_go_away = self.params.get_free().copy()109      cur_weights = self.example_weights.copy()110      grads = d2Obj_dParamsdWeights(some_example_weights,111                                    self.params.get_free())112      self.params.set_free(array_box_go_away)113    return grads114  def compute_dParams_dWeights(self, some_example_weights,115                               solver_method='cholesky',116                               non_fixed_dims=None,117                               rank=-1,118                               **kwargs):119    '''120    sets self.jacobian = dParams_dxn for each datapoint x_n121    rank = -1 uses a full-rank matrix solve (i.e. np.linalg.solve on the full122      Hessian). A positive integer uses a low rank approximation in123      inverse_hessian_vector_product  124      125    '''126    if non_fixed_dims is None:127      non_fixed_dims = np.arange(self.params.get_free().shape[0])128    if len(non_fixed_dims) == 0:129      self.dParams_dWeights = np.zeros((0,some_example_weights.shape[0]))130      return131    132    dObj_dParams = autograd.jacobian(self.weighted_model_objective, argnum=1)133    d2Obj_dParams2 = autograd.jacobian(dObj_dParams, argnum=1)134    d2Obj_dParamsdWeights = autograd.jacobian(dObj_dParams, argnum=0)135    136    # Have to re-copy this into self.params after every autograd call, as137    #  autograd turns self.params.get_free() into an ArrayBox (whereas we want138    #  it to be a numpy array)139    #array_box_go_away = self.params.get_free().copy()140    #cur_weights = self.example_weights.copy()141    start = time.time()142    grads = self.compute_gradients(some_example_weights)143    X = self.training_data.X144    if solver_method == 'cholesky':145      eval_reg_hess = autograd.hessian(self.regularization)146      tmp = self.params.get_free().copy()147      reg_hess = eval_reg_hess(self.params.get_free())148      reg_hess[-1,:] = 0.0149      reg_hess[:,-1] = 0.0150      self.params.set_free(tmp)151      self.dParams_dWeights = -solvers.ihvp_cholesky(grads,152                                                     X,153                                                     self.D2,154                                                regularizer_hessian=reg_hess)155    elif solver_method == 'agarwal':156      eval_reg_hess = autograd.hessian(self.regularization)157      tmp = self.params.get_free().copy()158      reg_hess = eval_reg_hess(self.params.get_free())159      reg_hess[-1,:] = 0.0160      reg_hess[:,-1] = 0.0161      self.params.set_free(tmp)162      self.dParams_dWeights = -solvers.ihvp_agarwal(grads,163                                                     X,164                                                    self.D2,165                                                    regularizer_hessian=reg_hess,166                                                    **kwargs)167    elif solver_method == 'lanczos':168      print('NOTE lanczos currently assumes l2 regularization')169      self.dParams_dWeights = -solvers.ihvp_exactEvecs(grads,170                                                       X,171                                                       self.D2,172                                                       rank=rank,173                                                       L2Lambda=self.L2Lambda)174    elif solver_method == 'tropp':175      print('NOTE tropp currently assumes l2 regularization')176      self.dParams_dWeights = -solvers.ihvp_tropp(grads,177                                                  X,178                                                  self.D2,179                                                  L2Lambda=self.L2Lambda,180                                                  rank=rank)181                                                  182      183      184    #self.params.set_free(array_box_go_away)185    #self.example_weights = cur_weights186    self.non_fixed_dims = non_fixed_dims187  def retrain_with_weights(self, new_example_weights,188                           doIJAppx=False, doNSAppx=False,189                           label=None,190                           is_cv=False,191                           non_fixed_dims=None,192                           **kwargs):193    '''194    in_place: updates weights and params based on the new data195    Can do things a bit more efficiently if it's cross-validation; you actually196    don't need to multiply (KxN) times (Nx1) vector; just select the components197    that have been left out198    ''' 199    if doIJAppx: # i.e. infinitesimal jackknife approx200      delta_example_weights = new_example_weights - self.example_weights201      if is_cv and False:202        left_out_inds = np.where(delta_example_weights == 0)203        new_params = self.params.get_free()204        new_params += self.dParams_dWeights[:,left_out_inds].sum(axis=1)205      else:206        if non_fixed_dims is None:207          new_params = self.params.get_free() + self.dParams_dWeights.dot(208            delta_example_weights)209        else:210          new_params = self.params.get_free()211          new_params[non_fixed_dims] += self.dParams_dWeights.dot(212            delta_example_weights)213    elif doNSAppx: # i.e., Newton step based approx214      if is_cv and self.is_a_glm: # Can do rank-1 update215        n = np.where(new_example_weights != 1)[0]216        new_params = self.params.get_free().copy()217        new_params[non_fixed_dims] += self.loocv_rank_one_updates[n,:].squeeze()218      else:219        self.compute_dParams_dWeights(new_example_weights,220                                      non_fixed_dims=non_fixed_dims)221        delta_example_weights = new_example_weights - self.example_weights222        new_params = self.params.get_free().copy()223        new_params[non_fixed_dims] += self.dParams_dWeights.dot(224          delta_example_weights)225    else: # non-approximate: re-fit the model226      curr_params = copy.copy(self.params.get_free())227      curr_example_weights = copy.copy(self.example_weights)228      self.example_weights = new_example_weights229      self.fit(**kwargs)230      new_params = copy.copy(self.params.get_free())231      232    self.params.set_free(new_params)233    return new_params234  235  def predict_probability(self, X):236    pass237  def get_error(self, test_data, metric):238    pass239  def get_single_datapoint_hessian(self, n):240    X = self.training_data.X241    Y = self.training_data.Y242    weights = self.example_weights243    self.training_data.X = X[n].reshape(1,-1)244    self.training_data.Y = Y[n]245    self.example_weights = np.ones(1)246    array_box_go_away = copy.copy(self.params.get_free())247    dObj_dParams = autograd.jacobian(self.weighted_model_objective, argnum=1)248    d2Obj_dParams2 = autograd.jacobian(dObj_dParams, argnum=1)249    hess_n = d2Obj_dParams2(self.example_weights, self.params.get_free())250    self.params.set_free(array_box_go_away)251    self.training_data.X = X252    self.training_data.Y = Y253    self.example_weights = weights254    return hess_n255  def get_single_datapoint_hvp(self, n, vec):256    '''257    Returns Hessian.dot(vec), where the Hessian is the Hessian of the objective258       function with just datapoint n259    '''    260    X = self.training_data.X261    Y = self.training_data.Y262    weights = self.example_weights263    self.training_data.X = X[n].reshape(1,-1)264    self.training_data.Y = Y[n]265    self.example_weights = np.ones(1)266    267    array_box_go_away = copy.copy(self.params.get_free())268    eval_hvp = autograd.hessian_vector_product(self.weighted_model_objective,269                                               argnum=1)270    hess_n_dot_vec = eval_hvp(self.example_weights, self.params.get_free(), vec)271    272    self.params.set_free(array_box_go_away)273    self.training_data.X = X274    self.training_data.Y = Y275    self.example_weights = weights276    return hess_n_dot_vec277  def get_all_data_hvp(self, vec):278    '''279    Returns Hessian.dot(vec), where the Hessian is the Hessian of the objective280       function with all the data.281    '''282    array_box_go_away = copy.copy(self.params.get_free())283    eval_hvp = autograd.hessian_vector_product(self.weighted_model_objective,284                                               argnum=1)285    hvp = eval_hvp(self.example_weights, self.params.get_free(), vec)286    287    self.params.set_free(array_box_go_away)288    return hvp289  def compute_hessian(self):290    dObj_dParams = autograd.jacobian(self.weighted_model_objective, argnum=1)291    d2Obj_dParams2 = autograd.jacobian(dObj_dParams, argnum=1)292    array_box_go_away = self.params.get_free().copy()293    hessian = d2Obj_dParams2(self.example_weights, self.params.get_free())294    self.params.set_free(array_box_go_away)295    self.hessian = hessian296  def compute_restricted_hessian_and_dParamsdWeights(self, dims, weights,297                                                     comp_dParams_dWeights=True):298    '''299    Computes the dims.shape[0] by dims.shape[0] Hessian only along the entries300    in dims (used when using l_1 regularization)301    '''302    theta0 = self.params.get_free()303    304    # Objective to differentiate just along the dimensions specified305    def lowDimObj(weights, thetaOnDims, thetaOffDims, invPerm):306      allDims = np.append(dims, offDims)307      thetaFull = np.append(thetaOnDims, thetaOffDims)[invPerm]308      return self.weighted_model_objective(weights, thetaFull)309    310    offDims = np.setdiff1d(np.arange(self.params.get_free().shape[0]), dims)311    thetaOnDims = theta0[dims]312    thetaOffDims = theta0[offDims]313    # lowDimObj will concatenate thetaOnDims, thetaOffDims, then needs to314    #  un-permute them into the original theta.315    allDims = np.append(dims, offDims)316    invPerm = np.zeros(theta0.shape[0], dtype=np.int32)317    for i, idx in enumerate(allDims):318      invPerm[idx] = i319    evalHess = autograd.hessian(lowDimObj, argnum=1)320    array_box_go_away = self.params.get_free().copy()321    restricted_hess = evalHess(weights,322                               thetaOnDims,323                               thetaOffDims,324                               invPerm)325    self.params.set_free(theta0)326    dObj_dParams = autograd.jacobian(lowDimObj, argnum=1)327    d2Obj_dParamsdWeights = autograd.jacobian(dObj_dParams, argnum=0)328    if comp_dParams_dWeights:329      restricted_dParamsdWeights = d2Obj_dParamsdWeights(weights,330                                                         thetaOnDims,331                                                         thetaOffDims,332                                                         invPerm)333      return restricted_hess, restricted_dParamsdWeights334    else:335      return restricted_hess336    337      338  def hessian_inverse_vector_product(self, vec, hessian_scaling,339                                     S1=None, S2=None, method='stochastic'):340    '''341    From Agarwal et. al. "Second-order stochastic optimization for machine342       learning in linear time." 2017. 343    Not clear that this provides good accuracy in a reasonable amount of time.344    '''345    N = self.training_data.X.shape[0]346    D = vec.shape[0]347    if S1 is None and S2 is None:348      S1 = int(np.ceil(np.sqrt(N)/10))349      S2 = int(np.ceil(10*np.sqrt(N)))350    hivpEsts = np.zeros((S1,D))351    for ii in range(S1):352      hivpEsts[ii] = vec353      for n in range(1,S2):354        idx = np.random.choice(N)355        #H_n_prod_prev = self.get_single_datapoint_hvp(idx, hivpEsts[ii]) * N356        #H_n_prod_prev /= hessian_scaling357        H_n_prod_prev = self.get_all_data_hvp(hivpEsts[ii]) / hessian_scaling358        hivpEsts[ii] = vec + hivpEsts[ii] - H_n_prod_prev359    return np.mean(hivpEsts, axis=0) / hessian_scaling360  def stochastic_hessian_inverse(self, hessian_scaling, S1=None, S2=None):361    '''362    From Agarwal et. al. "Second-order stochastic optimization for machine363       learning in linear time." 2017. 364    Not clear that this provides good accuracy in a reasonable amount of time.365    '''366    self.compute_derivs()367    X = self.training_data.X368    N = self.training_data.X.shape[0]369    D = self.params.get_free().shape[0]370    if S1 is None and S2 is None:371      S1 = int(np.sqrt(N)/10)372      S2 = int(10*np.sqrt(N))373    if self.regularization is not None:374      evalRegHess = autograd.hessian(self.regularization)375      paramsCpy = self.params.get_free().copy()376      regHess = evalRegHess(self.params.get_free())377      regHess[-1,-1] = 0.0378      self.params.set_free(paramsCpy)379      380    hinvEsts = np.zeros((S1,D,D))381    for ii in range(S1):382      hinvEsts[ii] = np.eye(D)383      for n in range(1,S2):384        idx = np.random.choice(N)385        H_n = np.outer(X[idx],X[idx]) * self.D2[idx] * N + regHess386        if np.linalg.norm(H_n) >= hessian_scaling*0.9999:387          print(np.linalg.norm(H_n))388        #H_n = self.get_single_datapoint_hessian(idx) * N389        H_n /= hessian_scaling390        hinvEsts[ii] = np.eye(D) + (np.eye(D) - H_n).dot(hinvEsts[ii])391    return np.mean(hinvEsts, axis=0) / hessian_scaling392  def compute_loocv_rank_one_updates(self,393                                     non_fixed_dims=None,394                                     **kwargs):395    '''396    When the model is a GLM and you're doing approximate LOOCV with the Newton397    step approximation, rank one matrix inverse updates allow you to use only398    O(D^3), rather than O(ND^3) computation.399    '''400    X = self.training_data.X401    N = X.shape[0]402    if non_fixed_dims is None:403      non_fixed_dims = np.arange(self.params.get_free().shape[0])404    if len(non_fixed_dims) == 0:405      self.loocv_rank_one_updates = np.zeros((N,0))406      return407      408        409    X_S = X[:,non_fixed_dims]410    hivps = self.inverse_hessian_vector_product(X.T, **kwargs).T411    X_S_hivps = np.einsum('nd,nd->n', X_S, hivps)412    updates = 1 + (self.D2 * X_S_hivps) / (1 - self.D2 * X_S_hivps)413    self.loocv_rank_one_updates = (updates*self.D1)[:,np.newaxis] * hivps414  def inverse_hessian_vector_product(self, b,415                                     solver_method='cholesky',416                                     rank=1,417                                     **kwargs):418    X = self.training_data.X419    if solver_method == 'cholesky':420      eval_reg_hess = autograd.hessian(self.regularization)421      tmp = self.params.get_free().copy()422      reg_hess = eval_reg_hess(self.params.get_free())423      reg_hess[-1,:] = 0.0424      reg_hess[:,-1] = 0.0425      self.params.set_free(tmp)426      return solvers.ihvp_cholesky(b,427                                   X,428                                   self.D2,429                                   regularizer_hessian=reg_hess)430    elif solver_method == 'agarwal':431      eval_reg_hess = autograd.hessian(self.regularization)432      tmp = self.params.get_free().copy()433      reg_hess = eval_reg_hess(self.params.get_free())434      reg_hess[-1,:] = 0.0435      reg_hess[:,-1] = 0.0436      self.params.set_free(tmp)437      return solvers.ihvp_agarwal(b,438                                  X,439                                  self.D2,440                                  regularizer_hessian=reg_hess,441                                  **kwargs)442    elif solver_method == 'lanczos':443      print('NOTE lanczos currently assumes l2 regularization')444      return solvers.ihvp_exactEvecs(b,445                                     X,446                                     self.D2,447                                     rank=rank,448                                     L2Lambda=self.L2Lambda)449    elif solver_method == 'tropp':450      print('NOTE tropp currently assumes l2 regularization')451      return solvers.ihvp_tropp(b,452                                X,453                                self.D2,454                                L2Lambda=self.L2Lambda,455                                rank=rank)456    457      458class PoissonRegressionModel(GeneralizedLinearModel):459  '''460  Poisson regression with:461           y_n ~ Poi( log(1 + e^{<x_n, \theta>}) )462  '''463  def __init__(self, *args, **kwargs):464    super(PoissonRegressionModel, self).__init__(*args, **kwargs)465  def eval_objective(self, free_params):466    self.params.set_free(free_params)467    params = self.params['w'].get()468    Y = self.training_data.Y469    params_x = np.dot(self.training_data.X, params)470    M = np.maximum(params_x, 0.0)471    lam = np.log(np.exp(0-M) + np.exp(params_x-M)) + M472    ret = Y*np.log(lam + 1e-15) - lam473    ll = (-(ret*self.example_weights).sum())474    return ll + self.regularization(params)475  def get_error(self, test_data, metric="mse"):476    if metric == "mse":477      X = test_data.X478      Y = test_data.Y479      params = self.params['w'].get()480      params_x = np.dot(X, params)481      stacked = np.stack([params_x, np.zeros(params_x.shape[0])], axis=0)482      lam = scipy.special.logsumexp(stacked, axis=0)483      Yhat = lam484      return np.mean((Yhat-Y)**2)485  def compute_derivs(self):486    '''487    lazy slow AD-based implementation ... should actually hand-compute488      these for any serious use.489    '''490    Y = self.training_data.Y491    z = self.training_data.X.dot(self.params.get_free())492    f = lambda z, Y: -(Y*np.log(np.log1p(np.exp(z))) - np.log1p(np.exp(z)))493    grad = autograd.grad(f, argnum=0)494    grad2 = autograd.grad(grad)495    self.D1 = np.zeros(Y.shape[0])496    self.D2 = np.zeros(Y.shape[0])497    for n in range(Y.shape[0]):498      self.D1[n] = grad(z[n], Y[n])499      self.D2[n] = grad2(z[n], Y[n])500      501class ExponentialPoissonRegressionModel(GeneralizedLinearModel):502  '''503  Poisson regression with:504           y_n ~ Poi( e^{<x_n, \theta>} )505  '''506  def __init__(self, *args, **kwargs):507    self.L1Lambda = None508    super(ExponentialPoissonRegressionModel, self).__init__(*args, **kwargs)509    510  def eval_objective(self, free_params):511    self.params.set_free(free_params)512    params = self.params['w'].get()513    Y = self.training_data.Y514    params_x_bias = np.dot(self.training_data.X, params)515    ret = Y*params_x_bias - np.exp(params_x_bias)516    ll = (-(ret*self.example_weights).sum())517    518    return ll + self.regularization(params)519  def fit(self, warm_start=True, label=None, save=False,520          use_glmnet=False,521          **kwargs):522    '''523    Note: use_glmnet only works with CV weights (i.e. all 0 or 1)524    '''525    if not use_glmnet:526      super(ExponentialPoissonRegressionModel, self).fit(warm_start,527                                                         label,528                                                         save,529                                                         **kwargs)530    elif use_glmnet:531      from glmnet_py import glmnet532      lambdau = np.array([self.L1Lambda,])533      inds = self.example_weights.astype(np.bool)534      x = self.training_data.X[inds,:].copy()535      y = self.training_data.Y[inds].copy().astype(np.float)536      fit = glmnet(x=x,537                   y=y,538                   family='poisson',539                   standardize=False,540                   lambdau=lambdau,541                   thresh=1e-20,542                   maxit=10e4,543                   alpha=1.0,544      )545             546  def compute_derivs(self):547    '''548    For use from fitL1.py.549    '''550    Y = self.training_data.Y551    params = self.params.get_free()552    #exp_params_X = np.exp(self.training_data.X.dot(self.params['w'].get()))553    exp_params_X = np.exp(self.training_data.X.dot(params))554    self.D1 = -(Y - exp_params_X)555    self.D2 = -(-exp_params_X)556      557  def get_error(self, test_data, metric="mse"):558    if metric == "mse":559      X = test_data.X560      Y = test_data.Y561      params = self.params['w'].get()562      params_x_bias = np.dot(X, params)563      lam = np.exp(params_x_bias)564      Yhat = lam565      return np.mean((Yhat-Y)**2)566  def compute_bounds(self):567    '''568    Used for low-rank CV paper. Assumes l2 regularization.569    Note these bounds are assuming the objective is **not** scaled570      by 1/N571    '''572    X = self.training_data.X573    D1 = self.D1574    D2 = self.D2575    lam = self.L2Lambda576    thetaHat = self.params.get_free()577    thetaThetanBnd = 1/(lam) * np.abs(D1) * np.linalg.norm(X, axis=1)578    exactBnd = np.abs(X.dot(thetaHat)) + thetaThetanBnd * np.linalg.norm(X, axis=1)579    MnBnd = np.exp(exactBnd)580    LipBnd = ((np.linalg.norm(X, axis=1)**2).sum() - np.linalg.norm(X, axis=1)**2) * MnBnd581    self.IJBnd = LipBnd / (lam**3)  * D1**2 * np.linalg.norm(X, axis=1)**3 + 1/lam**2 * D2 * np.abs(D1) * np.linalg.norm(X, axis=1)**4582    self.NSBnd = LipBnd / (lam**3) * D1**2 * np.linalg.norm(X, axis=1)**3583    584    585class LogisticRegressionModel(GeneralizedLinearModel):586  587  def __init__(self, *args, **kwargs):588    super(LogisticRegressionModel, self).__init__(*args, **kwargs)589  def fit(self, warm_start=True, label=None, save=False,590          use_glmnet=False,591          **kwargs):592    '''593    Note: use_glmnet only works with CV weights (i.e. all 0 or 1)594    '''595    596    if not use_glmnet:597      super(LogisticRegressionModel, self).fit(warm_start,598                                               label,599                                               save,600                                               **kwargs)601    elif use_glmnet:602      from glmnet_py import glmnet603      lambdau = np.array([self.L1Lambda / self.training_data.X.shape[0],])604      inds = self.example_weights.astype(np.bool)605      x = self.training_data.X[inds,:-1].copy()606      y = self.training_data.Y[inds].copy().astype(np.float)607      y[np.where(y==-1)] = 0.0608      fit = glmnet(x=x,609                   y=y,610                   family='binomial',611                   standardize=True,612                   lambdau=lambdau,613                   thresh=1e-10,614                   maxit=10e3,615                   alpha=1.0,616      )617      self.params.set_free(np.append(fit['beta'], 0))618      return619  def eval_objective(self, free_params):620    self.params.set_free(free_params)621    params = self.params['w'].get()622    X = self.training_data.X623    Y = self.training_data.Y624    paramsXY = -Y * (np.dot(X, params))625    M = np.maximum(paramsXY, 0)626    log_likelihood = -(np.log(np.exp(0-M) + np.exp(paramsXY-M)) + M)627    return ( -(log_likelihood*self.example_weights).sum() +628             self.regularization(params) )629  630  def predict_probability(self, X):631    return utils.sigmoid(X, self.params.get_free())632  def predict_target(self, X):633    probs = self.predict_probability(X)634    probs[np.where(probs > .5)] = 1635    probs[np.where(probs <= .5)] = -1636    return probs637  def compute_derivs(self):638    '''639    For use from fitL1.py640    '''641    Y = self.training_data.Y642    params = self.params.get_free()643    exp_params_XY = np.exp(Y *644              self.training_data.X.dot(params))645    self.D1 = -Y/ (1 + exp_params_XY)646    self.D2 = -Y*self.D1 - (self.D1)**2647    648  def get_error(self, test_data, metric='log_likelihood'):649    if metric == "accuracy":650      # change Y_Test to 01 if required651      return 1.0 * np.where(652        self.predict_target(test_data.X) != test_data.Y)[0].shape[0] / test_data.N653    elif metric == 'log_likelihood':654      train_data = self.training_data655      weights = self.example_weights656      self.training_data = test_data657      self.example_weights = np.ones(test_data.X.shape[0])658      nll = self.eval_objective(self.params.get_free())659      nll -= self.regularization(self.params.get_free())660      self.training_data = train_data661      self.example_weights = weights662      return nll / test_data.X.shape[0]663class LinearRegressionModel(GeneralizedLinearModel):664  def __init__(self, *args, **kwargs):665    super(LinearRegressionModel, self).__init__(*args, **kwargs)666  def eval_objective(self, free_params):667    '''668    Objective that we minimize; \sum_n w_n f(x_n, \theta) + ||\theta||_2669    '''670    self.params.set_free(free_params)671    params = self.params['w'].get()672    params_x = np.dot(self.training_data.X, params)673    sq_error = (self.training_data.Y - params_x)**2 * self.example_weights674    return sq_error.sum() + self.regularization(params[:-1])675  def get_error(self, test_data, metric="mse"):676    if metric == "mse":677      Yhat = np.dot(test_data.X, self.params.get_free())678      Y = test_data.Y679      return np.mean((Yhat - Y)**2)680  def compute_derivs(self):681    '''682    First and second derivatives of link function, used in fitL1.py683    '''684    Y = self.training_data.Y685    params_x = self.training_data.X.dot(self.params.get_free())686    self.D1 = -2*(Y - params_x)687    self.D2 = 2*np.ones(Y.shape[0])688    689  def fit(self, warm_start=True, label=None, save=False,690          use_glmnet=False, **kwargs):691    '''692    Note: this only works with CV weights (i.e. all 0 or 1)693    '''694    if not use_glmnet:695      super(LinearRegressionModel, self).fit(warm_start,696                                             label,697                                             save,698                                             **kwargs)699    elif use_glmnet:700      from glmnet_py import glmnet701      inds = self.example_weights.astype(np.bool)702      x = self.training_data.X[inds,:].copy()703      y = self.training_data.Y[inds].copy().astype(np.float)704      lambdau = np.array([self.L1Lambda/(2*x.shape[0]),])705      706      fit = glmnet(x=x[:,:-1],707                   y=y,708                   family='gaussian',709                   standardize=True,710                   lambdau=lambdau,711                   thresh=1e-10,712                   maxit=10e4,713                   alpha=1.0,714      )715      self.params.set_free(np.append(np.squeeze(fit['beta']), fit['a0']))716      717class ProbitRegressionModel(GeneralizedLinearModel):718  def __init__(self, *args, **kwargs):719    super(ProbitRegressionModel, self).__init__(*args, **kwargs)720  def eval_objective(self, free_params):721    self.params.set_free(free_params)722    params_no_bias = self.params['w'].get()[:-1]723    bias = self.params['w'].get()[-1]724    y_x_params = self.training_data.Y * (725      np.dot(self.training_data.X, params_no_bias) + bias)726    log_likelihood = \727                autograd.scipy.stats.norm.logcdf(y_x_params) * self.example_weights728    return -(log_likelihood).sum() + self.regularization(params_no_bias)729  def predict_probability(self, X):730    params_no_bias = self.params.get_free()[:-1]731    bias = self.params.get_free()[-1]732    return autograd.scipy.stats.norm.cdf(X.dot(params_no_bias) + bias)733  def predict_target(self, X):734    probs = self.predict_probability(X)735    probs[np.where(probs > .5)] = 1736    probs[np.where(probs <= .5)] = -1737    return probs738  def get_error(self, test_data, metric="log_likelihood"):739    if metric == "accuracy":740      # change Y_Test to 01 if required741      return np.where(742        self.predict_target(test_data.X) != test_data.Y)[0].shape[0] / test_data.N743    elif metric == 'log_likelihood':744      train_data = self.training_data745      weights = self.example_weights746      self.training_data = test_data747      self.example_weights = np.ones(test_data.X.shape[0])748      nll = self.eval_objective(self.params.get_free())749      self.training_data = train_data...

axiom.py

Source:axiom.py

...41    for i in range(len(axioms)):42        if check(expr, axioms[i], dict()):43            return i + 144    return 045def get_free(expr, variables, free_variables):46    if type(expr) is Variable:47        if expr not in variables.keys():48            free_variables.add(expr.val)49    elif type(expr) is Predicate:50        for val in expr.val:51            get_free(val, variables, free_variables)52    elif type(expr) is Any or type(expr) is Exists:53        if expr.var not in variables.keys():54            variables[expr.var] = 155        else:56            variables[expr.var] += 157        get_free(expr.val, variables, free_variables)58        variables[expr.var] -= 159        if variables[expr.var] == 0:60            variables.pop(expr.var)61    elif isinstance(expr, Unary):62        return get_free(expr.val, variables, free_variables)63    elif isinstance(expr, Binary):64        return get_free(expr.left, variables, free_variables) | get_free(expr.right, variables,65                                                                         free_variables)66    return free_variables67def get_free_variables(expr):68    s = set()69    get_free(expr, dict(), s)70    return s71def free_subtract(axiom, expr, variable, locked_variables, variables):72    if type(axiom) is Variable:73        if axiom != variable:74            return axiom == expr75        if axiom.val in locked_variables:76            return axiom == expr77        else:78            if axiom not in variables:79                freeVariables = get_free_variables(expr)80                if len(freeVariables.intersection(locked_variables)) != 0:81                    return False82                variables[axiom] = expr83                return True...

Automation Testing Tutorials

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