Module Expr Str Import alias identifier ImportFrom alias alias ImportFrom alias Import alias ImportFrom alias alias alias ImportFrom alias alias ImportFrom alias alias alias ImportFrom alias alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias alias ImportFrom alias ImportFrom alias alias alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias Assign NameStore ListLoad Str Str FunctionDef arguments args NameParam NameParam defaults body Expr Str Return Call AttributeLoad NameLoad attr ListComp Call AttributeLoad NameLoad attr comprehension NameStore BinOpAdd NameLoad NameLoad decorator_list ClassDef bases Call AttributeLoad NameLoad attr NameLoad NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults body Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list NameLoad FunctionDef arguments args NameParam NameParam defaults body Expr Str For NameStore Call NameLoad BinOpSub AttributeLoad NameLoad attr Num body Assign TupleStore NameStore NameStore NameStore SubscriptLoad AttributeLoad NameLoad attr Index NameLoad Assign SubscriptStore AttributeLoad NameLoad attr Index NameLoad Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Slice NameLoad NameLoad NameLoad Assign TupleStore NameStore NameStore SubscriptLoad AttributeLoad NameLoad attr Index NameLoad Assign SubscriptStore AttributeLoad NameLoad attr Index NameLoad SubscriptLoad NameLoad Slice NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults NameLoad body Expr Str Assign NameStore SubscriptLoad NameLoad Index AttributeLoad NameLoad attr For NameStore Call NameLoad BinOpSub AttributeLoad NameLoad attr Num body Assign SubscriptStore NameLoad Index BinOpAdd NameLoad Num Call NameLoad SubscriptLoad NameLoad Index NameLoad SubscriptLoad AttributeLoad NameLoad attr Index NameLoad AugAssignAdd SubscriptStore NameLoad Index BinOpAdd NameLoad Num SubscriptLoad AttributeLoad NameLoad attr Index NameLoad If CompareNotEq BinOpAdd NameLoad Num BinOpSub AttributeLoad NameLoad attr Num body Assign SubscriptStore NameLoad Index BinOpAdd NameLoad Num Call NameLoad SubscriptLoad NameLoad Index BinOpAdd NameLoad Num If NameLoad body Assign NameStore SubscriptLoad NameLoad Index AttributeLoad NameLoad attr Assign SubscriptStore NameLoad Index BinOpAdd NameLoad Num Call NameLoad SubscriptLoad NameLoad Index BinOpAdd NameLoad Num Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults body Expr Str Assign SubscriptStore NameLoad Index NameLoad Call NameLoad AttributeLoad SubscriptLoad NameLoad Index NameLoad attr SubscriptLoad NameLoad Index NameLoad AugAssignAdd SubscriptStore NameLoad Index NameLoad BinOpMult AttributeLoad NameLoad attr SubscriptLoad AttributeLoad NameLoad attr Index NameLoad AugAssignDiv SubscriptStore NameLoad Index NameLoad NameLoad Assign SubscriptStore NameLoad Index NameLoad Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index NameLoad Num Return TupleLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults body Expr Str Expr Call AttributeLoad NameLoad attr NameLoad Assign TupleStore NameStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad AugAssignAdd AttributeStore NameLoad attr Num Assign NameStore Call NameLoad NameLoad NameLoad Return TupleLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults body Expr Str Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call SubscriptLoad NameLoad Index AttributeLoad NameLoad attr NameLoad SubscriptLoad NameLoad Index Num Assign NameStore Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr ListComp Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr comprehension NameStore AttributeLoad NameLoad attr AugAssignAdd NameStore BinOpDiv BinOpMult BinOpMult Num AttributeLoad NameLoad attr NameLoad NameLoad Assign NameStore BinOpSub AttributeLoad NameLoad attr Num Assign NameStore BinOpSub NameLoad SubscriptLoad NameLoad Index Num Assign SubscriptStore NameLoad Index NameLoad UnaryOpUSub NameLoad Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad For NameStore Call NameLoad BinOpSub AttributeLoad NameLoad attr Num Num Num body Assign SubscriptStore NameLoad Index BinOpSub NameLoad Num Call NameLoad SubscriptLoad NameLoad Index NameLoad AttributeLoad SubscriptLoad AttributeLoad NameLoad attr Index NameLoad attr Assign NameStore SubscriptLoad NameLoad Index AttributeLoad NameLoad attr AugAssignMult SubscriptStore NameLoad Index BinOpSub NameLoad Num Call NameLoad SubscriptLoad NameLoad Index NameLoad Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr BinOpSub NameLoad Num NameLoad NameLoad NameLoad NameLoad NameLoad Return TupleLoad NameLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Assign AttributeStore NameLoad attr Num Assign AttributeStore NameLoad attr Num Assign AttributeStore NameLoad attr SubscriptLoad AttributeLoad NameLoad attr Index Num Assign AttributeStore NameLoad attr Call NameLoad NameLoad If UnaryOpNot Call NameLoad NameLoad NameLoad body Assign AttributeStore NameLoad attr Str orelse If CompareEq AttributeLoad AttributeLoad NameLoad attr attr Str body Assign AttributeStore NameLoad attr Str orelse Assign AttributeStore NameLoad attr Str If CompareEq AttributeLoad NameLoad attr Str body Assign AttributeStore NameLoad attr Str Assign AttributeStore NameLoad attr ListLoad Assign AttributeStore NameLoad attr ListLoad For NameStore Call NameLoad BinOpSub AttributeLoad NameLoad attr Num body Assign NameStore Call NameLoad AttributeLoad NameLoad attr Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index NameLoad SubscriptLoad NameLoad Index BinOpAdd NameLoad Num NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad If CompareIn AttributeLoad NameLoad attr NameLoad body Assign AttributeStore NameLoad attr ListLoad Assign AttributeStore NameLoad attr Num If AttributeLoad NameLoad attr body Assign AttributeStore NameLoad attr ListLoad Assign AttributeStore NameLoad attr UnaryOpUSub AttributeLoad NameLoad attr orelse Assign AttributeStore NameLoad attr AttributeLoad NameLoad attr decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults body If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Call AttributeLoad NameLoad attr BinOpDiv Num BinOpAdd NameLoad NameLoad orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Call AttributeLoad NameLoad attr BinOpDiv Num BinOpAdd NameLoad NameLoad orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Call AttributeLoad NameLoad attr BinOpDiv Num BinOpAdd NameLoad NameLoad orelse Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr UnaryOpUSub NameLoad NameLoad TupleLoad NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr UnaryOpUSub NameLoad NameLoad NameLoad Return TupleLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad body Assign NameStore AttributeLoad NameLoad attr If UnaryOpNot Call NameLoad NameLoad Str body Assign NameStore ListLoad NameLoad Assign NameStore Call NameLoad NameLoad Expr Call AttributeLoad NameLoad attr If Call AttributeLoad NameLoad attr CompareLtE Call AttributeLoad NameLoad attr NameLoad Num body Raise Call NameLoad BinOpMod Str NameLoad Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad Assign TupleStore NameStore NameStore AttributeLoad NameLoad attr If CompareEq AttributeLoad NameLoad attr Num body Assign NameStore Call AttributeLoad NameLoad attr TupleLoad Num Num Assign AttributeStore NameLoad attr SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore BinOpAdd BinOpAdd ListLoad NameLoad NameLoad ListLoad AttributeLoad NameLoad attr If BoolOpOr UnaryOpNot Call NameLoad NameLoad Str BoolOpAnd UnaryOpNot AttributeLoad NameLoad attr UnaryOpNot NameLoad body Expr Call AttributeLoad NameLoad attr NameLoad NameLoad If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore NameLoad orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Call NameLoad Num NameLoad orelse If BoolOpOr CompareLt AttributeLoad NameLoad attr Num CompareGt AttributeLoad NameLoad attr NameLoad body Expr Call AttributeLoad NameLoad attr Str Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Num NameLoad Assign NameStore ListLoad NameLoad Expr Call AttributeLoad NameLoad attr GeneratorExp Call AttributeLoad NameLoad attr TupleLoad NameLoad NameLoad comprehension NameStore SubscriptLoad NameLoad Slice Num Assign NameStore ListComp Call AttributeLoad NameLoad attr NameLoad comprehension NameStore NameLoad Assign NameStore ListComp Call AttributeLoad NameLoad attr TupleLoad NameLoad NameLoad comprehension TupleStore NameStore NameStore Call NameLoad SubscriptLoad NameLoad Slice Num SubscriptLoad NameLoad Slice Num Assign NameStore ListComp Call AttributeLoad NameLoad attr NameLoad comprehension NameStore SubscriptLoad NameLoad Slice Num If CompareIn AttributeLoad NameLoad attr NameLoad body Expr Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad orelse If CompareEq AttributeLoad NameLoad attr Str body Expr Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam defaults body If UnaryOpNot Call NameLoad AttributeLoad NameLoad attr NameLoad body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If CompareLtE AttributeLoad NameLoad attr Num body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If CompareLt AttributeLoad NameLoad attr Num body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If BoolOpAnd CompareIn AttributeLoad NameLoad attr ListLoad Str Str Str CompareLtE AttributeLoad NameLoad attr Num body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If BoolOpOr CompareGt AttributeLoad NameLoad attr Num CompareLt AttributeLoad NameLoad attr Num body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If UnaryOpNot Call NameLoad AttributeLoad NameLoad attr NameLoad body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If UnaryOpNot Call NameLoad AttributeLoad NameLoad attr NameLoad body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If BoolOpOr CompareLt AttributeLoad NameLoad attr Num CompareGtE AttributeLoad NameLoad attr Num body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If BoolOpOr CompareLt AttributeLoad NameLoad attr Num CompareGtE AttributeLoad NameLoad attr Num body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If BoolOpOr CompareLt AttributeLoad NameLoad attr Num CompareGtE AttributeLoad NameLoad attr Num body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If CompareLtE AttributeLoad NameLoad attr Num body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr Assign NameStore ListLoad Str Str Str If CompareNotIn AttributeLoad NameLoad attr NameLoad body Raise Call NameLoad BinOpMod Str TupleLoad AttributeLoad NameLoad attr NameLoad If CompareNotIn AttributeLoad NameLoad attr ListLoad Str Str Str body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr If CompareNotIn AttributeLoad NameLoad attr BinOpAdd NameLoad ListLoad Str body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults body Assign AttributeStore NameLoad attr ListLoad Assign AttributeStore NameLoad attr ListLoad Assign NameStore Num For NameStore Call NameLoad BinOpSub AttributeLoad NameLoad attr Num body Assign TupleStore NameStore NameStore TupleLoad SubscriptLoad NameLoad Index NameLoad SubscriptLoad NameLoad Index BinOpAdd NameLoad Num Assign NameStore BinOpAdd NameLoad BinOpMult NameLoad NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr TupleLoad NameLoad NameLoad TupleLoad NameLoad NameLoad Assign NameStore NameLoad For NameStore Call NameLoad BinOpSub AttributeLoad NameLoad attr Num body Assign NameStore BinOpAdd NameLoad SubscriptLoad NameLoad Index BinOpAdd NameLoad Num Expr Call AttributeLoad AttributeLoad NameLoad attr attr TupleLoad NameLoad NameLoad Assign NameStore NameLoad Assign NameStore Call NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr If BoolOpOr CompareIs AttributeLoad NameLoad attr NameLoad CompareGtE AttributeLoad NameLoad attr Num body Assign NameStore Num orelse Assign NameStore Num Assign TupleStore NameStore AttributeStore NameLoad attr NameStore Call NameLoad keyword NameLoad keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr keyword NameLoad keyword AttributeLoad NameLoad attr keyword TupleLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Expr Call AttributeLoad NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults body Assign NameStore Call NameLoad AttributeLoad NameLoad attr If BoolOpOr UnaryOpNot NameLoad UnaryOpNot Call NameLoad NameLoad Str body Assign NameStore BinOpAdd AttributeLoad NameLoad attr AttributeLoad NameLoad attr If CompareEq AttributeLoad NameLoad attr Str body Assign AttributeStore NameLoad attr Call NameLoad NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr orelse If CompareEq AttributeLoad NameLoad attr Str body Assign AttributeStore NameLoad attr Call NameLoad NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign NameStore BoolOpAnd AttributeLoad NameLoad attr UnaryOpNot NameLoad If NameLoad body Assign TupleStore NameStore NameStore NameStore NameStore Call NameLoad NameLoad NameLoad keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr If Call NameLoad NameLoad NameLoad body Assign NameStore Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad orelse Assign NameStore NameLoad Assign NameStore NameLoad Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Call NameLoad Num NameLoad orelse Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Num NameLoad TryExcept body For NameStore Call NameLoad AttributeLoad NameLoad attr body Assign TupleStore NameStore NameStore Call NameLoad NameLoad NameLoad keyword NameLoad Assign NameStore Num For NameStore Call NameLoad NameLoad NameLoad body Assign SubscriptStore NameLoad Index Num SubscriptLoad NameLoad Index NameLoad Assign TupleStore NameStore NameStore NameStore Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index NameLoad SubscriptLoad NameLoad Index NameLoad NameLoad NameLoad NameLoad NameLoad AugAssignAdd NameStore BinOpMult NameLoad BinOpSub AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign NameStore BinOpAdd NameLoad NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad AugAssignAdd AttributeStore NameLoad attr Num Assign AttributeStore NameLoad attr BinOpDiv NameLoad SubscriptLoad AttributeLoad NameLoad attr Index Num AugAssignAdd AttributeStore NameLoad attr NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr AttributeLoad NameLoad attr If AttributeLoad NameLoad attr body Print BinOpMod Str TupleLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr Expr Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr AttributeLoad NameLoad attr If CompareGt AttributeLoad NameLoad attr Num body If NameLoad body Assign NameStore BinOpMod Str AttributeLoad NameLoad attr orelse Assign NameStore BinOpMod Str AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad AttributeLoad NameLoad attr If NameLoad body Break orelse Assign AttributeStore NameLoad attr Num If NameLoad body Break If CompareEq AttributeLoad NameLoad attr AttributeLoad NameLoad attr body Expr Call AttributeLoad NameLoad attr BinOpMod Str TupleLoad NameLoad handlers ExceptHandler type NameLoad body Pass If NameLoad body Assign AttributeStore NameLoad attr AttributeLoad NameLoad attr Assign AttributeStore NameLoad attr AttributeLoad NameLoad attr decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults body If NameLoad body Expr Call AttributeLoad AttributeLoad NameLoad attr attr Call AttributeLoad NameLoad attr NameLoad NameLoad If AttributeLoad NameLoad attr body Print BinOpMod Str SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num If CompareLt NameLoad BinOpAdd AttributeLoad NameLoad attr AttributeLoad NameLoad attr body AugAssignAdd AttributeStore NameLoad attr Num orelse Assign AttributeStore NameLoad attr Num If CompareGt NameLoad AttributeLoad NameLoad attr body Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr ListComp Call AttributeLoad NameLoad attr comprehension NameStore AttributeLoad NameLoad attr Assign AttributeStore NameLoad attr ListComp Call AttributeLoad NameLoad attr comprehension NameStore AttributeLoad NameLoad attr orelse If CompareGt SubscriptLoad AttributeLoad NameLoad attr Index Num BinOpSub AttributeLoad NameLoad attr AttributeLoad NameLoad attr body AugAssignAdd AttributeStore NameLoad attr Num orelse Assign AttributeStore NameLoad attr Num If CompareLt SubscriptLoad AttributeLoad NameLoad attr Index Num AttributeLoad NameLoad attr body Assign AttributeStore NameLoad attr SubscriptLoad AttributeLoad NameLoad attr Index Num decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Return Call AttributeLoad NameLoad attr NameLoad NameLoad keyword NameLoad decorator_list FunctionDef arguments args NameParam defaults body Expr Str If CompareNotIn AttributeLoad NameLoad attr NameLoad body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr Return AttributeLoad NameLoad attr decorator_list NameLoad FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad body Return Call AttributeLoad NameLoad attr NameLoad NameLoad keyword NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call NameLoad NameLoad keyword ListLoad Str Str Str Assign NameStore AttributeLoad NameLoad attr If UnaryOpNot Call NameLoad NameLoad Str body Assign NameStore ListLoad NameLoad Assign NameStore Call NameLoad NameLoad Assign NameStore BinOpAdd BinOpAdd ListLoad SubscriptLoad AttributeLoad NameLoad attr Index Num NameLoad ListLoad AttributeLoad NameLoad attr Assign NameStore ListLoad NameLoad For NameStore Call NameLoad BinOpSub AttributeLoad NameLoad attr Num body Expr Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr TupleLoad SubscriptLoad AttributeLoad NameLoad attr Index Num SubscriptLoad NameLoad Index BinOpAdd NameLoad Num Expr Call AttributeLoad NameLoad attr NameLoad keyword NameLoad Assign NameStore SubscriptLoad NameLoad Index Num Return NameLoad decorator_list decorator_list ClassDef bases NameLoad NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults TupleLoad Num Str Str Num Str Str Num Num Num NameLoad NameLoad Num NameLoad NameLoad Num NameLoad NameLoad Num Num Num Num body Assign NameStore Call NameLoad NameLoad NameLoad Expr Call AttributeLoad NameLoad attr keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword Str keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad Assign AttributeStore NameLoad attr Call NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults body Assign TupleStore NameStore NameStore Call NameLoad NameLoad NameLoad keyword ListLoad Str Str Str keyword NameLoad If BoolOpAnd CompareEq AttributeLoad NameLoad attr Num CompareEq SubscriptLoad AttributeLoad NameLoad attr Index Num Num body Assign NameStore Call NameLoad NameLoad keyword NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad If BoolOpOr UnaryOpNot Call NameLoad NameLoad Str UnaryOpNot NameLoad body Assign AttributeStore NameLoad attr AttributeLoad AttributeLoad NameLoad attr attr orelse Assign NameStore AttributeLoad AttributeLoad NameLoad attr attr If UnaryOpNot Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr NameLoad AttributeLoad NameLoad attr body Raise Call NameLoad BinOpMod Str TupleLoad AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad Return TupleLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Expr Call NameLoad NameLoad Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad If CompareEq AttributeLoad NameLoad attr Num body Return Call AttributeLoad NameLoad attr orelse Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Expr Call NameLoad NameLoad Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call SubscriptLoad NameLoad Index AttributeLoad NameLoad attr NameLoad Return Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad decorator_list FunctionDef arguments args NameParam defaults body Expr Str If CompareNotIn AttributeLoad NameLoad attr NameLoad body Raise Call NameLoad BinOpMod Str AttributeLoad NameLoad attr Return AttributeLoad NameLoad attr decorator_list NameLoad FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad body Expr Call NameLoad NameLoad NameLoad Expr Call AttributeLoad Call NameLoad NameLoad NameLoad attr NameLoad NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad Return Call AttributeLoad NameLoad attr NameLoad keyword NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad If CompareEq AttributeLoad NameLoad attr Num body Assign NameStore Call NameLoad NameLoad Return AttributeLoad Call AttributeLoad NameLoad attr ListLoad BinOpSub Num NameLoad NameLoad attr orelse Return Call NameLoad NameLoad decorator_list decorator_list ClassDef bases NameLoad NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults TupleLoad Num Str Str Num Str Str Num Num Num NameLoad NameLoad Num NameLoad NameLoad Num NameLoad NameLoad Num Num Num Num body Assign NameStore Call NameLoad NameLoad NameLoad Expr Call AttributeLoad NameLoad attr keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword Str keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Expr Call NameLoad NameLoad Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad If CompareEq SubscriptLoad AttributeLoad NameLoad attr Index Num Num body Return Call AttributeLoad NameLoad attr Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults body Assign TupleStore NameStore NameStore Call NameLoad NameLoad NameLoad keyword ListLoad Str Str Str keyword NameLoad keyword NameLoad If BoolOpAnd CompareEq AttributeLoad NameLoad attr Num CompareEq SubscriptLoad AttributeLoad NameLoad attr Index Num Num body Assign NameStore Call NameLoad NameLoad keyword NameLoad Return TupleLoad NameLoad NameLoad decorator_list decorator_list
N/A N/A Multi-layer Perceptron
 N/A numpy np abc ABCMeta abstractmethod scipy.optimize fmin_l_bfgs_b N/A warnings base BaseEstimator ClassifierMixin RegressorMixin _base logistic softmax _base ACTIVATIONS DERIVATIVES LOSS_FUNCTIONS _stochastic_optimizers SGDOptimizer AdamOptimizer model_selection train_test_split externals six preprocessing LabelBinarizer utils gen_batches check_random_state utils shuffle utils check_array check_X_y column_or_1d exceptions ConvergenceWarning utils.extmath safe_sparse_dot utils.validation check_is_fitted utils.multiclass _check_partial_fit_first_call N/A _STOCHASTIC_ALGOS N/A sgd adam _pack N/A N/A coefs_ intercepts_ N/A N/A N/A Pack the parameters into a single vector. N/A N/A N/A np hstack N/A N/A N/A l ravel N/A l N/A coefs_ intercepts_ N/A BaseMultilayerPerceptron N/A N/A N/A six with_metaclass ABCMeta BaseEstimator N/A N/A Base class for MLP classification and regression.

    Warning: This class should not be used directly.
    Use derived classes instead.
     __init__ N/A N/A self hidden_layer_sizes activation algorithm alpha batch_size learning_rate learning_rate_init power_t max_iter loss shuffle random_state tol verbose warm_start momentum nesterovs_momentum early_stopping validation_fraction beta_1 beta_2 epsilon N/A N/A N/A N/A self activation activation N/A N/A self algorithm algorithm N/A N/A self alpha alpha N/A N/A self batch_size batch_size N/A N/A self learning_rate learning_rate N/A N/A self learning_rate_init learning_rate_init N/A N/A self power_t power_t N/A N/A self max_iter max_iter N/A N/A self loss loss N/A N/A self hidden_layer_sizes hidden_layer_sizes N/A N/A self shuffle shuffle N/A N/A self random_state random_state N/A N/A self tol tol N/A N/A self verbose verbose N/A N/A self warm_start warm_start N/A N/A self momentum momentum N/A N/A self nesterovs_momentum nesterovs_momentum N/A N/A self early_stopping early_stopping N/A N/A self validation_fraction validation_fraction N/A N/A self beta_1 beta_1 N/A N/A self beta_2 beta_2 N/A N/A self epsilon epsilon N/A abstractmethod _unpack N/A N/A self packed_parameters N/A N/A N/A Extract the coefficients and intercepts from packed_parameters. N/A i N/A range N/A N/A self n_layers_ 1 N/A N/A N/A start end shape N/A N/A self _coef_indptr N/A i N/A N/A N/A self coefs_ N/A i N/A N/A np reshape N/A packed_parameters N/A start end shape N/A N/A start end N/A N/A self _intercept_indptr N/A i N/A N/A N/A self intercepts_ N/A i N/A packed_parameters N/A start end N/A _forward_pass N/A N/A self activations with_output_activation N/A True N/A N/A Perform a forward pass on the network by computing the values
        of the neurons in the hidden layers and the output layer.

        Parameters
        ----------
        activations: list, length = n_layers - 1
            The ith element of the list holds the values of the ith layer.

        with_output_activation : bool, default True
            If True, the output passes through the output activation
            function, which is either the softmax function or the
            logistic function
         N/A hidden_activation N/A ACTIVATIONS N/A N/A self activation N/A i N/A range N/A N/A self n_layers_ 1 N/A N/A N/A activations N/A N/A i 1 N/A safe_sparse_dot N/A activations N/A i N/A N/A self coefs_ N/A i N/A N/A activations N/A N/A i 1 N/A N/A self intercepts_ N/A i N/A N/A N/A i 1 N/A N/A self n_layers_ 1 N/A N/A N/A activations N/A N/A i 1 N/A hidden_activation N/A activations N/A N/A i 1 N/A with_output_activation N/A N/A output_activation N/A ACTIVATIONS N/A N/A self out_activation_ N/A N/A activations N/A N/A i 1 N/A output_activation N/A activations N/A N/A i 1 N/A activations N/A _compute_loss_grad N/A N/A self layer n_samples activations deltas coef_grads intercept_grads N/A N/A N/A Compute the gradient of loss with respect to coefs and intercept for
        specified layer.

        This function does backpropagation for the specified one layer.
         N/A N/A coef_grads N/A layer N/A safe_sparse_dot N/A N/A activations N/A layer T N/A deltas N/A layer N/A N/A coef_grads N/A layer N/A N/A self alpha N/A N/A self coefs_ N/A layer N/A N/A coef_grads N/A layer n_samples N/A N/A intercept_grads N/A layer N/A N/A np mean N/A deltas N/A layer 0 N/A N/A coef_grads intercept_grads N/A _loss_grad_lbfgs N/A N/A self packed_coef_inter X y activations deltas coef_grads intercept_grads N/A N/A N/A Compute the MLP loss function and its corresponding derivatives
        with respect to the different parameters given in the initialization.

        Returned gradients are packed in a single vector so it can be used
        in l-bfgs

        Parameters
        ----------
        packed_parameters : array-like
            A vector comprising the flattened coefficients and intercepts.

        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        y : array-like, shape (n_samples,)
            The target values.

        activations: list, length = n_layers - 1
            The ith element of the list holds the values of the ith layer.

        deltas : list, length = n_layers - 1
            The ith element of the list holds the difference between the
            activations of the i + 1 layer and the backpropagated error.
            More specifically, deltas are gradients of loss with respect to z
            in each layer, where z = wx + b is the value of a particular layer
            before passing through the activation function

        coef_grad : list, length = n_layers - 1
            The ith element contains the amount of change used to update the
            coefficient parameters of the ith layer in an iteration.

        intercept_grads : list, length = n_layers - 1
            The ith element contains the amount of change used to update the
            intercept parameters of the ith layer in an iteration.

        Returns
        -------
        loss : float
        grad : array-like, shape (number of nodes of all layers,)

         N/A N/A N/A self _unpack packed_coef_inter N/A N/A loss coef_grads intercept_grads N/A N/A self _backprop X y activations deltas coef_grads intercept_grads N/A N/A self n_iter_ 1 N/A grad N/A _pack coef_grads intercept_grads N/A N/A loss grad N/A _backprop N/A N/A self X y activations deltas coef_grads intercept_grads N/A N/A N/A Compute the MLP loss function and its corresponding derivatives
        with respect to each parameter: weights and bias vectors.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        y : array-like, shape (n_samples,)
            The target values.

        activations: list, length = n_layers - 1
             The ith element of the list holds the values of the ith layer.

        deltas : list, length = n_layers - 1
            The ith element of the list holds the difference between the
            activations of the i + 1 layer and the backpropagated error.
            More specifically, deltas are gradients of loss with respect to z
            in each layer, where z = wx + b is the value of a particular layer
            before passing through the activation function

        coef_grad : list, length = n_layers - 1
            The ith element contains the amount of change used to update the
            coefficient parameters of the ith layer in an iteration.

        intercept_grads : list, length = n_layers - 1
            The ith element contains the amount of change used to update the
            intercept parameters of the ith layer in an iteration.

        Returns
        -------
        loss : float
        coef_grads : list, length = n_layers - 1
        intercept_grads : list, length = n_layers - 1
         N/A n_samples N/A N/A X shape N/A 0 N/A activations N/A N/A self _forward_pass activations N/A loss N/A N/A LOSS_FUNCTIONS N/A N/A self loss y N/A activations N/A -1 N/A values N/A N/A np sum N/A N/A np array N/A N/A N/A np dot N/A N/A s ravel N/A N/A s ravel N/A s N/A self coefs_ N/A loss N/A N/A N/A 0.5 N/A self alpha values n_samples N/A last N/A N/A self n_layers_ 2 N/A diff N/A y N/A activations N/A -1 N/A N/A deltas N/A last N/A diff N/A N/A coef_grads intercept_grads N/A N/A self _compute_loss_grad last n_samples activations deltas coef_grads intercept_grads N/A i N/A range N/A N/A self n_layers_ 2 0 -1 N/A N/A N/A deltas N/A N/A i 1 N/A safe_sparse_dot N/A deltas N/A i N/A N/A N/A self coefs_ N/A i T N/A derivative N/A DERIVATIVES N/A N/A self activation N/A N/A deltas N/A N/A i 1 N/A derivative N/A activations N/A i N/A N/A coef_grads intercept_grads N/A N/A self _compute_loss_grad N/A i 1 n_samples activations deltas coef_grads intercept_grads N/A N/A loss coef_grads intercept_grads N/A _initialize N/A N/A self y layer_units N/A N/A N/A N/A self n_iter_ 0 N/A N/A self t_ 0 N/A N/A self n_outputs_ N/A N/A y shape N/A 1 N/A N/A self n_layers_ N/A len layer_units N/A N/A N/A isinstance self ClassifierMixin N/A N/A N/A self out_activation_ identity N/A N/A N/A N/A N/A self label_binarizer_ y_type_ multiclass N/A N/A N/A self out_activation_ softmax N/A N/A N/A self out_activation_ logistic N/A N/A N/A self loss log_loss N/A N/A N/A self loss binary_log_loss N/A N/A self coefs_ N/A N/A N/A self intercepts_ N/A N/A i N/A range N/A N/A self n_layers_ 1 N/A N/A rng N/A check_random_state N/A self random_state N/A N/A coef_init intercept_init N/A N/A self _init_coef N/A layer_units N/A i N/A layer_units N/A N/A i 1 rng N/A N/A N/A N/A self coefs_ append coef_init N/A N/A N/A N/A self intercepts_ append intercept_init N/A N/A N/A self algorithm _STOCHASTIC_ALGOS N/A N/A N/A self loss_curve_ N/A N/A N/A self _no_improvement_count 0 N/A N/A self early_stopping N/A N/A N/A self validation_scores_ N/A N/A N/A self best_validation_score_ N/A N/A np inf N/A N/A N/A self best_loss_ N/A np inf N/A _init_coef N/A N/A self fan_in fan_out rng N/A N/A N/A N/A N/A self activation logistic N/A N/A init_bound N/A N/A np sqrt N/A 2.0 N/A fan_in fan_out N/A N/A N/A N/A self activation tanh N/A N/A init_bound N/A N/A np sqrt N/A 6.0 N/A fan_in fan_out N/A N/A N/A N/A self activation relu N/A N/A init_bound N/A N/A np sqrt N/A 6.0 N/A fan_in fan_out N/A N/A N/A ValueError N/A Unknown activation function %s N/A self activation N/A coef_init N/A N/A rng uniform N/A init_bound init_bound N/A fan_in fan_out N/A intercept_init N/A N/A rng uniform N/A init_bound init_bound fan_out N/A N/A coef_init intercept_init N/A _fit N/A N/A self X y incremental N/A False N/A N/A hidden_layer_sizes N/A self hidden_layer_sizes N/A N/A N/A hasattr hidden_layer_sizes __iter__ N/A N/A hidden_layer_sizes N/A hidden_layer_sizes N/A hidden_layer_sizes N/A list hidden_layer_sizes N/A N/A N/A self _validate_hyperparameters N/A N/A N/A np any N/A N/A N/A np array hidden_layer_sizes 0 N/A N/A N/A ValueError N/A hidden_layer_sizes must be > 0, got %s. hidden_layer_sizes N/A N/A X y N/A N/A self _validate_input X y incremental N/A N/A n_samples n_features N/A X shape N/A N/A N/A y ndim 1 N/A N/A y N/A N/A y reshape N/A -1 1 N/A N/A self n_outputs_ N/A N/A y shape N/A 1 N/A layer_units N/A N/A N/A n_features hidden_layer_sizes N/A N/A self n_outputs_ N/A N/A N/A N/A hasattr self coefs_ N/A N/A N/A self warm_start N/A incremental N/A N/A N/A N/A self _initialize y layer_units N/A N/A N/A self algorithm l-bfgs N/A N/A batch_size n_samples N/A N/A N/A N/A self batch_size auto N/A N/A batch_size N/A min 200 n_samples N/A N/A N/A N/A N/A self batch_size 1 N/A N/A self batch_size n_samples N/A N/A N/A N/A warnings warn Got `batch_size` less than 1 or larger than sample size. It is going to be clipped N/A batch_size N/A N/A np clip N/A self batch_size 1 n_samples N/A activations N/A X N/A N/A N/A activations extend N/A N/A N/A np empty N/A batch_size n_fan_out N/A n_fan_out N/A layer_units N/A 1 N/A deltas N/A N/A N/A np empty_like a_layer N/A a_layer activations N/A coef_grads N/A N/A N/A np empty N/A n_fan_in_ n_fan_out_ N/A N/A n_fan_in_ n_fan_out_ N/A zip N/A layer_units N/A -1 N/A layer_units N/A 1 N/A intercept_grads N/A N/A N/A np empty n_fan_out_ N/A n_fan_out_ N/A layer_units N/A 1 N/A N/A N/A self algorithm _STOCHASTIC_ALGOS N/A N/A N/A N/A self _fit_stochastic X y activations deltas coef_grads intercept_grads layer_units incremental N/A N/A N/A N/A self algorithm l-bfgs N/A N/A N/A N/A self _fit_lbfgs X y activations deltas coef_grads intercept_grads layer_units N/A self N/A _validate_hyperparameters N/A N/A self N/A N/A N/A N/A N/A isinstance N/A self shuffle bool N/A N/A N/A ValueError N/A shuffle must be either True or False, got %s. N/A self shuffle N/A N/A N/A self max_iter 0 N/A N/A N/A ValueError N/A max_iter must be > 0, got %s. N/A self max_iter N/A N/A N/A self alpha 0.0 N/A N/A N/A ValueError N/A alpha must be >= 0, got %s. N/A self alpha N/A N/A N/A N/A self learning_rate N/A constant invscaling adaptive N/A N/A self learning_rate_init 0.0 N/A N/A N/A ValueError N/A learning_rate_init must be > 0, got %s. N/A self learning_rate N/A N/A N/A N/A self momentum 1 N/A N/A self momentum 0 N/A N/A N/A ValueError N/A momentum must be >= 0 and <= 1, got %s N/A self momentum N/A N/A N/A isinstance N/A self nesterovs_momentum bool N/A N/A N/A ValueError N/A nesterovs_momentum must be either True or False, got %s. N/A self nesterovs_momentum N/A N/A N/A isinstance N/A self early_stopping bool N/A N/A N/A ValueError N/A early_stopping must be either True or False, got %s. N/A self early_stopping N/A N/A N/A N/A self validation_fraction 0 N/A N/A self validation_fraction 1 N/A N/A N/A ValueError N/A validation_fraction must be >= 0 and < 1, got %s N/A self validation_fraction N/A N/A N/A N/A self beta_1 0 N/A N/A self beta_1 1 N/A N/A N/A ValueError N/A beta_1 must be >= 0 and < 1, got %s N/A self beta_1 N/A N/A N/A N/A self beta_2 0 N/A N/A self beta_2 1 N/A N/A N/A ValueError N/A beta_2 must be >= 0 and < 1, got %s N/A self beta_2 N/A N/A N/A self epsilon 0.0 N/A N/A N/A ValueError N/A epsilon must be > 0, got %s. N/A self epsilon N/A supported_activations N/A logistic tanh relu N/A N/A N/A self activation supported_activations N/A N/A N/A ValueError N/A The activation '%s' is not supported. Supported activations are %s. N/A N/A self activation supported_activations N/A N/A N/A self learning_rate N/A constant invscaling adaptive N/A N/A N/A ValueError N/A learning rate %s is not supported.  N/A self learning_rate N/A N/A N/A self algorithm N/A _STOCHASTIC_ALGOS N/A l-bfgs N/A N/A N/A ValueError N/A The algorithm %s is not supported.  N/A self algorithm N/A _fit_lbfgs N/A N/A self X y activations deltas coef_grads intercept_grads layer_units N/A N/A N/A N/A self _coef_indptr N/A N/A N/A self _intercept_indptr N/A N/A start 0 N/A i N/A range N/A N/A self n_layers_ 1 N/A N/A N/A n_fan_in n_fan_out N/A N/A layer_units N/A i N/A layer_units N/A N/A i 1 N/A end N/A start N/A n_fan_in n_fan_out N/A N/A N/A N/A self _coef_indptr append N/A start end N/A n_fan_in n_fan_out N/A start end N/A i N/A range N/A N/A self n_layers_ 1 N/A N/A end N/A start N/A layer_units N/A N/A i 1 N/A N/A N/A N/A self _intercept_indptr append N/A start end N/A start end N/A packed_coef_inter N/A _pack N/A self coefs_ N/A self intercepts_ N/A N/A N/A N/A self verbose True N/A N/A self verbose 1 N/A N/A iprint 1 N/A N/A iprint -1 N/A N/A optimal_parameters N/A self loss_ d N/A fmin_l_bfgs_b x0 packed_coef_inter func N/A self _loss_grad_lbfgs maxfun N/A self max_iter iprint iprint pgtol N/A self tol args N/A X y activations deltas coef_grads intercept_grads N/A N/A N/A self _unpack optimal_parameters N/A _fit_stochastic N/A N/A self X y activations deltas coef_grads intercept_grads layer_units incremental N/A N/A N/A rng N/A check_random_state N/A self random_state N/A N/A N/A incremental N/A N/A hasattr self _optimizer N/A N/A params N/A N/A self coefs_ N/A self intercepts_ N/A N/A N/A self algorithm sgd N/A N/A N/A self _optimizer N/A SGDOptimizer params N/A self learning_rate_init N/A self learning_rate N/A self momentum N/A self nesterovs_momentum N/A self power_t N/A N/A N/A N/A self algorithm adam N/A N/A N/A self _optimizer N/A AdamOptimizer params N/A self learning_rate_init N/A self beta_1 N/A self beta_2 N/A self epsilon N/A early_stopping N/A N/A self early_stopping N/A incremental N/A early_stopping N/A N/A N/A X X_val y y_val N/A train_test_split X y random_state N/A self random_state test_size N/A self validation_fraction N/A N/A isinstance self ClassifierMixin N/A N/A y_val N/A N/A N/A self label_binarizer_ inverse_transform y_val N/A N/A X_val None N/A y_val None N/A n_samples N/A N/A X shape N/A 0 N/A N/A N/A self batch_size auto N/A N/A batch_size N/A min 200 n_samples N/A N/A batch_size N/A N/A np clip N/A self batch_size 1 n_samples N/A N/A N/A it N/A range N/A self max_iter N/A N/A N/A X y N/A shuffle X y random_state rng N/A accumulated_loss 0.0 N/A batch_slice N/A gen_batches n_samples batch_size N/A N/A N/A activations N/A 0 N/A X N/A batch_slice N/A N/A batch_loss coef_grads intercept_grads N/A N/A self _backprop N/A X N/A batch_slice N/A y N/A batch_slice activations deltas coef_grads intercept_grads N/A accumulated_loss N/A batch_loss N/A N/A batch_slice stop N/A batch_slice start N/A grads N/A coef_grads intercept_grads N/A N/A N/A N/A self _optimizer update_params grads N/A N/A self n_iter_ 1 N/A N/A self loss_ N/A accumulated_loss N/A N/A X shape N/A 0 N/A N/A self t_ n_samples N/A N/A N/A N/A self loss_curve_ append N/A self loss_ N/A N/A self verbose N/A N/A N/A Iteration %d, loss = %.8f N/A N/A self n_iter_ N/A self loss_ N/A N/A N/A self _update_no_improvement_count early_stopping X_val y_val N/A N/A N/A N/A self _optimizer iteration_ends N/A self t_ N/A N/A N/A self _no_improvement_count 2 N/A N/A early_stopping N/A N/A msg N/A Validation score did not improve more than tol=%f for two consecutive epochs. N/A self tol N/A N/A msg N/A Training loss did not improve more than tol=%f for two consecutive epochs. N/A self tol N/A is_stopping N/A N/A N/A self _optimizer trigger_stopping msg N/A self verbose N/A is_stopping N/A N/A N/A N/A N/A self _no_improvement_count 0 N/A incremental N/A N/A N/A N/A N/A self n_iter_ N/A self max_iter N/A N/A N/A N/A warnings warn N/A Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet. N/A ConvergenceWarning N/A N/A N/A KeyboardInterrupt N/A N/A N/A early_stopping N/A N/A N/A self coefs_ N/A self _best_coefs N/A N/A self intercepts_ N/A self _best_intercepts N/A _update_no_improvement_count N/A N/A self early_stopping X_val y_val N/A N/A N/A early_stopping N/A N/A N/A N/A N/A self validation_scores_ append N/A N/A self score X_val y_val N/A N/A self verbose N/A N/A N/A Validation score: %f N/A N/A self validation_scores_ N/A -1 N/A last_valid_score N/A N/A self validation_scores_ N/A -1 N/A N/A last_valid_score N/A N/A self best_validation_score_ N/A self tol N/A N/A N/A self _no_improvement_count 1 N/A N/A N/A self _no_improvement_count 0 N/A N/A last_valid_score N/A self best_validation_score_ N/A N/A N/A self best_validation_score_ last_valid_score N/A N/A self _best_coefs N/A N/A N/A c copy N/A c N/A self coefs_ N/A N/A self _best_intercepts N/A N/A N/A i copy N/A i N/A self intercepts_ N/A N/A N/A N/A N/A self loss_curve_ N/A -1 N/A N/A self best_loss_ N/A self tol N/A N/A N/A self _no_improvement_count 1 N/A N/A N/A self _no_improvement_count 0 N/A N/A N/A N/A self loss_curve_ N/A -1 N/A self best_loss_ N/A N/A N/A self best_loss_ N/A N/A self loss_curve_ N/A -1 N/A fit N/A N/A self X y N/A N/A N/A Fit the model to data matrix X and target y.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        y : array-like, shape (n_samples,)
            The target values.

        Returns
        -------
        self : returns a trained MLP model.
         N/A N/A N/A self _fit X y incremental False N/A partial_fit N/A N/A self N/A N/A N/A Fit the model to data matrix X and target y.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        y : array-like, shape (n_samples,)
            The target values.

        Returns
        -------
        self : returns a trained MLP model.
         N/A N/A N/A self algorithm _STOCHASTIC_ALGOS N/A N/A N/A AttributeError N/A partial_fit is only available for stochasticoptimization algorithms. %s is not stochastic N/A self algorithm N/A N/A self _partial_fit N/A property _partial_fit N/A N/A self X y classes N/A None N/A N/A N/A N/A self _fit X y incremental True N/A _decision_scores N/A N/A self X N/A N/A N/A Predict using the trained model

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        Returns
        -------
        y_pred : array-like, shape (n_samples,) or (n_samples, n_outputs)
            The decision function of the samples for each class in the model.
         N/A X N/A check_array X accept_sparse N/A csr csc coo N/A hidden_layer_sizes N/A self hidden_layer_sizes N/A N/A N/A hasattr hidden_layer_sizes __iter__ N/A N/A hidden_layer_sizes N/A hidden_layer_sizes N/A hidden_layer_sizes N/A list hidden_layer_sizes N/A layer_units N/A N/A N/A N/A N/A X shape N/A 1 hidden_layer_sizes N/A N/A self n_outputs_ N/A activations N/A X N/A i N/A range N/A N/A self n_layers_ 1 N/A N/A N/A N/A activations append N/A N/A np empty N/A N/A N/A X shape N/A 0 N/A layer_units N/A N/A i 1 N/A N/A N/A self _forward_pass activations with_output_activation False N/A y_pred N/A activations N/A -1 N/A y_pred N/A N/A MLPClassifier N/A BaseMultilayerPerceptron ClassifierMixin N/A N/A Multi-layer Perceptron classifier.

    This algorithm optimizes the log-loss function using l-bfgs or gradient
    descent.

    Parameters
    ----------
    hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)
        The ith element represents the number of neurons in the ith
        hidden layer.

    activation : {'logistic', 'tanh', 'relu'}, default 'relu'
        Activation function for the hidden layer.

        - 'logistic', the logistic sigmoid function,
          returns f(x) = 1 / (1 + exp(-x)).

        - 'tanh', the hyperbolic tan function,
          returns f(x) = tanh(x).

        - 'relu', the rectified linear unit function,
          returns f(x) = max(0, x)

    algorithm : {'l-bfgs', 'sgd', 'adam'}, default 'adam'
        The algorithm for weight optimization.

        - 'l-bfgs' is an optimization algorithm in the family of
          quasi-Newton methods.

        - 'sgd' refers to stochastic gradient descent.

        - 'adam' refers to a stochastic gradient-based optimization algorithm
          proposed by Kingma, Diederik, and Jimmy Ba

        Note: The default algorithm 'adam' works pretty well on relatively
        large datasets (with thousands of training samples or more) in terms of
        both training time and validation score.
        For small datasets, however, 'l-bfgs' can converge faster and perform
        better.

    alpha : float, optional, default 0.0001
        L2 penalty (regularization term) parameter.

    batch_size : int, optional, default 'auto'
        Size of minibatches for stochastic optimizers.
        If the algorithm is 'l-bfgs', the classifier will not use minibatch.
        When set to "auto", `batch_size=min(200, n_samples)`

    learning_rate : {'constant', 'invscaling', 'adaptive'}, default 'constant'
        Learning rate schedule for weight updates.

        -'constant', is a constant learning rate given by
         'learning_rate_init'.

        -'invscaling' gradually decreases the learning rate ``learning_rate_`` at
          each time step 't' using an inverse scaling exponent of 'power_t'.
          effective_learning_rate = learning_rate_init / pow(t, power_t)

        -'adaptive', keeps the learning rate constant to
         'learning_rate_init' as long as training loss keeps decreasing.
         Each time two consecutive epochs fail to decrease training loss by at
         least tol, or fail to increase validation score by at least tol if
         'early_stopping' is on, the current learning rate is divided by 5.

         Only used when algorithm='sgd'.

    max_iter : int, optional, default 200
        Maximum number of iterations. The algorithm iterates until convergence
        (determined by 'tol') or this number of iterations.

    random_state : int or RandomState, optional, default None
        State or seed for random number generator.

    shuffle : bool, optional, default True
        Whether to shuffle samples in each iteration. Only used when
        algorithm='sgd' or 'adam'.

    tol : float, optional, default 1e-4
        Tolerance for the optimization. When the loss or score is not improving
        by at least tol for two consecutive iterations, unless `learning_rate`
        is set to 'adaptive', convergence is considered to be reached and
        training stops.

    learning_rate_init : double, optional, default 0.001
        The initial learning rate used. It controls the step-size
        in updating the weights. Only used when algorithm='sgd' or 'adam'.

    power_t : double, optional, default 0.5
        The exponent for inverse scaling learning rate.
        It is used in updating effective learning rate when the learning_rate
        is set to 'invscaling'. Only used when algorithm='sgd'.

    verbose : bool, optional, default False
        Whether to print progress messages to stdout.

    warm_start : bool, optional, default False
        When set to True, reuse the solution of the previous
        call to fit as initialization, otherwise, just erase the
        previous solution.

    momentum : float, default 0.9
        Momentum for gradient descent update. Should be between 0 and 1. Only
        used when algorithm='sgd'.

    nesterovs_momentum : boolean, default True
        Whether to use Nesterov's momentum. Only used when algorithm='sgd' and
        momentum > 0.

    early_stopping : bool, default False
        Whether to use early stopping to terminate training when validation
        score is not improving. If set to true, it will automatically set
        aside 10% of training data as validation and terminate training when
        validation score is not improving by at least tol for two consecutive
        epochs.
        Only effective when algorithm='sgd' or 'adam'

    validation_fraction : float, optional, default 0.1
        The proportion of training data to set aside as validation set for
        early stopping. Must be between 0 and 1.
        Only used if early_stopping is True

    beta_1 : float, optional, default 0.9
        Exponential decay rate for estimates of first moment vector in adam,
        should be in [0, 1). Only used when algorithm='adam'

    beta_2 : float, optional, default 0.999
        Exponential decay rate for estimates of second moment vector in adam,
        should be in [0, 1). Only used when algorithm='adam'

    epsilon : float, optional, default 1e-8
        Value for numerical stability in adam. Only used when algorithm='adam'

    Attributes
    ----------
    `classes_` : array or list of array of shape (n_classes,)
        Class labels for each output.

    `loss_` : float
        The current loss computed with the loss function.

    `label_binarizer_` : LabelBinarizer
        A LabelBinarizer object trained on the training set.

    `coefs_` : list, length n_layers - 1
        The ith element in the list represents the weight matrix corresponding
        to layer i.

    `intercepts_` : list, length n_layers - 1
        The ith element in the list represents the bias vector corresponding to
        layer i + 1.

    n_iter_ : int,
        The number of iterations the algorithm has ran.

    n_layers_ : int
        Number of layers.

    `n_outputs_` : int
        Number of outputs.

    `out_activation_` : string
        Name of the output activation function.

    Notes
    -----
    MLPClassifier trains iteratively since at each time step
    the partial derivatives of the loss function with respect to the model
    parameters are computed to update the parameters.

    It can also have a regularization term added to the loss function
    that shrinks model parameters to prevent overfitting.

    This implementation works with data represented as dense numpy arrays or
    sparse scipy arrays of floating point values.

    References
    ----------
    Hinton, Geoffrey E.
        "Connectionist learning procedures." Artificial intelligence 40.1
        (1989): 185-234.

    Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of
        training deep feedforward neural networks." International Conference
        on Artificial Intelligence and Statistics. 2010.

    He, Kaiming, et al. "Delving deep into rectifiers: Surpassing human-level
        performance on imagenet classification." arXiv preprint
        arXiv:1502.01852 (2015).

    Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic
        optimization." arXiv preprint arXiv:1412.6980 (2014).
     __init__ N/A N/A self hidden_layer_sizes activation algorithm alpha batch_size learning_rate learning_rate_init power_t max_iter shuffle random_state tol verbose warm_start momentum nesterovs_momentum early_stopping validation_fraction beta_1 beta_2 epsilon N/A N/A 100 relu adam 0.0001 auto constant 0.001 0.5 200 True None 0.0001 False False 0.9 True False 0.1 0.9 0.999 1e-08 N/A N/A sup N/A super MLPClassifier self N/A N/A N/A sup __init__ hidden_layer_sizes hidden_layer_sizes activation activation algorithm algorithm alpha alpha batch_size batch_size learning_rate learning_rate learning_rate_init learning_rate_init power_t power_t max_iter max_iter loss log_loss shuffle shuffle random_state random_state tol tol verbose verbose warm_start warm_start momentum momentum nesterovs_momentum nesterovs_momentum early_stopping early_stopping validation_fraction validation_fraction beta_1 beta_1 beta_2 beta_2 epsilon epsilon N/A N/A self label_binarizer_ N/A LabelBinarizer N/A _validate_input N/A N/A self X y incremental N/A N/A N/A N/A X y N/A check_X_y X y accept_sparse N/A csr csc coo multi_output True N/A N/A N/A N/A y ndim 2 N/A N/A N/A y shape N/A 1 1 N/A N/A y N/A column_or_1d y warn True N/A N/A N/A N/A self label_binarizer_ fit y N/A N/A N/A N/A hasattr self classes_ N/A incremental N/A N/A N/A self classes_ N/A N/A self label_binarizer_ classes_ N/A N/A classes N/A N/A self label_binarizer_ classes_ N/A N/A N/A N/A np all N/A N/A np in1d classes N/A self classes_ N/A N/A N/A ValueError N/A `y` has classes not in `self.classes_`. `self.classes_` has %s. 'y' has %s. N/A N/A self classes_ classes N/A y N/A N/A N/A self label_binarizer_ transform y N/A N/A X y N/A decision_function N/A N/A self X N/A N/A N/A Decision function of the mlp model

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        Returns
        -------
        y : array-like, shape (n_samples,) or (n_samples, n_classes)
            The values of decision function for each class in the model.
         N/A N/A check_is_fitted self coefs_ N/A y_scores N/A N/A self _decision_scores X N/A N/A N/A self n_outputs_ 1 N/A N/A N/A N/A y_scores ravel N/A N/A y_scores N/A predict N/A N/A self X N/A N/A N/A Predict using the multi-layer perceptron classifier

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        Returns
        -------
        y : array-like, shape (n_samples,) or (n_samples, n_classes)
            The predicted classes.
         N/A N/A check_is_fitted self coefs_ N/A y_scores N/A N/A self decision_function X N/A y_scores N/A N/A ACTIVATIONS N/A N/A self out_activation_ y_scores N/A N/A N/A N/A self label_binarizer_ inverse_transform y_scores N/A partial_fit N/A N/A self N/A N/A N/A Fit the model to data matrix X and target y.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        y : array-like, shape (n_samples,)
            The target values.

        classes : array, shape (n_classes)
            Classes across all calls to partial_fit.
            Can be obtained via `np.unique(y_all)`, where y_all is the
            target vector of the entire dataset.
            This argument is required for the first call to partial_fit
            and can be omitted in the subsequent calls.
            Note that y doesn't need to contain all labels in `classes`.

        Returns
        -------
        self : returns a trained MLP model.
         N/A N/A N/A self algorithm _STOCHASTIC_ALGOS N/A N/A N/A AttributeError N/A partial_fit is only available for stochasticoptimization algorithms. %s is not stochastic N/A self algorithm N/A N/A self _partial_fit N/A property _partial_fit N/A N/A self X y classes N/A None N/A N/A N/A _check_partial_fit_first_call self classes N/A N/A N/A N/A super MLPClassifier self _partial_fit X y N/A self N/A predict_log_proba N/A N/A self X N/A N/A N/A Return the log of probability estimates.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            The input data.

        Returns
        -------
        log_y_prob : array-like, shape (n_samples, n_classes)
            The predicted log-probability of the sample for each class
            in the model, where classes are ordered as they are in
            `self.classes_`. Equivalent to log(predict_proba(X))
         N/A y_prob N/A N/A self predict_proba X N/A N/A N/A np log y_prob out y_prob N/A predict_proba N/A N/A self X N/A N/A N/A Probability estimates.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        Returns
        -------
        y_prob : array-like, shape (n_samples, n_classes)
            The predicted probability of the sample for each class in the
            model, where classes are ordered as they are in `self.classes_`.
         N/A y_scores N/A N/A self decision_function X N/A N/A N/A y_scores ndim 1 N/A N/A y_scores N/A logistic y_scores N/A N/A N/A N/A np vstack N/A N/A 1 y_scores y_scores T N/A N/A N/A softmax y_scores N/A N/A MLPRegressor N/A BaseMultilayerPerceptron RegressorMixin N/A N/A Multi-layer Perceptron regressor.

    This algorithm optimizes the squared-loss using l-bfgs or gradient descent.

    Parameters
    ----------
    hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)
        The ith element represents the number of neurons in the ith
        hidden layer.

    activation : {'logistic', 'tanh', 'relu'}, default 'relu'
        Activation function for the hidden layer.

        - 'logistic', the logistic sigmoid function,
          returns f(x) = 1 / (1 + exp(-x)).

        - 'tanh', the hyperbolic tan function,
          returns f(x) = tanh(x).

        - 'relu', the rectified linear unit function,
          returns f(x) = max(0, x)

    algorithm : {'l-bfgs', 'sgd', 'adam'}, default 'adam'
        The algorithm for weight optimization.

        - 'l-bfgs' is an optimization algorithm in the family of
          quasi-Newton methods.

        - 'sgd' refers to stochastic gradient descent.

        - 'adam' refers to a stochastic gradient-based optimization algorithm
          proposed by Kingma, Diederik, and Jimmy Ba

        Note: The default algorithm 'adam' works pretty well on relatively
        large datasets (with thousands of training samples or more) in terms of
        both training time and validation score.
        For small datasets, however, 'l-bfgs' can converge faster and perform
        better.

    alpha : float, optional, default 0.0001
        L2 penalty (regularization term) parameter.

    batch_size : int, optional, default 'auto'
        Size of minibatches for stochastic optimizers.
        If the algorithm is 'l-bfgs', the classifier will not use minibatch.
        When set to "auto", `batch_size=min(200, n_samples)`

    learning_rate : {'constant', 'invscaling', 'adaptive'}, default 'constant'
        Learning rate schedule for weight updates.

        -'constant', is a constant learning rate given by
         'learning_rate_init'.

        -'invscaling' gradually decreases the learning rate ``learning_rate_`` at
          each time step 't' using an inverse scaling exponent of 'power_t'.
          effective_learning_rate = learning_rate_init / pow(t, power_t)

        -'adaptive', keeps the learning rate constant to
         'learning_rate_init' as long as training loss keeps decreasing.
         Each time two consecutive epochs fail to decrease training loss by at
         least tol, or fail to increase validation score by at least tol if
         'early_stopping' is on, the current learning rate is divided by 5.

         Only used when algorithm='sgd'.

    max_iter : int, optional, default 200
        Maximum number of iterations. The algorithm iterates until convergence
        (determined by 'tol') or this number of iterations.

    random_state : int or RandomState, optional, default None
        State or seed for random number generator.

    shuffle : bool, optional, default True
        Whether to shuffle samples in each iteration. Only used when
        algorithm='sgd' or 'adam'.

    tol : float, optional, default 1e-4
        Tolerance for the optimization. When the loss or score is not improving
        by at least tol for two consecutive iterations, unless `learning_rate`
        is set to 'adaptive', convergence is considered to be reached and
        training stops.

    learning_rate_init : double, optional, default 0.001
        The initial learning rate used. It controls the step-size
        in updating the weights. Only used when algorithm='sgd' or 'adam'.

    power_t : double, optional, default 0.5
        The exponent for inverse scaling learning rate.
        It is used in updating effective learning rate when the learning_rate
        is set to 'invscaling'. Only used when algorithm='sgd'.

    verbose : bool, optional, default False
        Whether to print progress messages to stdout.

    warm_start : bool, optional, default False
        When set to True, reuse the solution of the previous
        call to fit as initialization, otherwise, just erase the
        previous solution.

    momentum : float, default 0.9
        Momentum for gradient descent update.  Should be between 0 and 1. Only
        used when algorithm='sgd'.

    nesterovs_momentum : boolean, default True
        Whether to use Nesterov's momentum. Only used when algorithm='sgd' and
        momentum > 0.

    early_stopping : bool, default False
        Whether to use early stopping to terminate training when validation
        score is not improving. If set to true, it will automatically set
        aside 10% of training data as validation and terminate training when
        validation score is not improving by at least tol for two consecutive
        epochs.
        Only effective when algorithm='sgd' or 'adam'

    validation_fraction : float, optional, default 0.1
        The proportion of training data to set aside as validation set for
        early stopping. Must be between 0 and 1.
        Only used if early_stopping is True

    beta_1 : float, optional, default 0.9
        Exponential decay rate for estimates of first moment vector in adam,
        should be in [0, 1). Only used when algorithm='adam'

    beta_2 : float, optional, default 0.999
        Exponential decay rate for estimates of second moment vector in adam,
        should be in [0, 1). Only used when algorithm='adam'

    epsilon : float, optional, default 1e-8
        Value for numerical stability in adam. Only used when algorithm='adam'

    Attributes
    ----------
    `loss_` : float
        The current loss computed with the loss function.

    `coefs_` : list, length n_layers - 1
        The ith element in the list represents the weight matrix corresponding
        to layer i.

    `intercepts_` : list, length n_layers - 1
        The ith element in the list represents the bias vector corresponding to
        layer i + 1.

    n_iter_ : int,
        The number of iterations the algorithm has ran.

    n_layers_ : int
        Number of layers.

    `n_outputs_` : int
        Number of outputs.

    `out_activation_` : string
        Name of the output activation function.

    Notes
    -----
    MLPRegressor trains iteratively since at each time step
    the partial derivatives of the loss function with respect to the model
    parameters are computed to update the parameters.

    It can also have a regularization term added to the loss function
    that shrinks model parameters to prevent overfitting.

    This implementation works with data represented as dense and sparse numpy
    arrays of floating point values.

    References
    ----------
    Hinton, Geoffrey E.
        "Connectionist learning procedures." Artificial intelligence 40.1
        (1989): 185-234.

    Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of
        training deep feedforward neural networks." International Conference
        on Artificial Intelligence and Statistics. 2010.

    He, Kaiming, et al. "Delving deep into rectifiers: Surpassing human-level
        performance on imagenet classification." arXiv preprint
        arXiv:1502.01852 (2015).

    Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic
        optimization." arXiv preprint arXiv:1412.6980 (2014).
     __init__ N/A N/A self hidden_layer_sizes activation algorithm alpha batch_size learning_rate learning_rate_init power_t max_iter shuffle random_state tol verbose warm_start momentum nesterovs_momentum early_stopping validation_fraction beta_1 beta_2 epsilon N/A N/A 100 relu adam 0.0001 auto constant 0.001 0.5 200 True None 0.0001 False False 0.9 True False 0.1 0.9 0.999 1e-08 N/A N/A sup N/A super MLPRegressor self N/A N/A N/A sup __init__ hidden_layer_sizes hidden_layer_sizes activation activation algorithm algorithm alpha alpha batch_size batch_size learning_rate learning_rate learning_rate_init learning_rate_init power_t power_t max_iter max_iter loss squared_loss shuffle shuffle random_state random_state tol tol verbose verbose warm_start warm_start momentum momentum nesterovs_momentum nesterovs_momentum early_stopping early_stopping validation_fraction validation_fraction beta_1 beta_1 beta_2 beta_2 epsilon epsilon N/A predict N/A N/A self X N/A N/A N/A Predict using the multi-layer perceptron model.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        Returns
        -------
        y : array-like, shape (n_samples, n_outputs)
            The predicted values.
         N/A N/A check_is_fitted self coefs_ N/A y_pred N/A N/A self _decision_scores X N/A N/A N/A N/A y_pred shape N/A 1 1 N/A N/A N/A N/A y_pred ravel N/A y_pred N/A _validate_input N/A N/A self X y incremental N/A N/A N/A N/A X y N/A check_X_y X y accept_sparse N/A csr csc coo multi_output True y_numeric True N/A N/A N/A N/A y ndim 2 N/A N/A N/A y shape N/A 1 1 N/A N/A y N/A column_or_1d y warn True N/A N/A X y N/A N/A