mage/python/node_classification.py at main · memgraph/mage · GitHub

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import os

from tqdm import tqdm

import typing

from datetime import datetime

from time import time

import mgp

import torch

from torch_geometric.nn import to_hetero

from torch_geometric.data import HeteroData

from mage.node_classification.models.gat import GAT

from mage.node_classification.models.gatv2 import GATv2

from mage.node_classification.models.sage import SAGE

from mage.node_classification.models.gatjk import GATJK

from mage.node_classification.utils.metrics import metrics

from mage.node_classification.utils.extract_from_database import extract_from_database

from mage.node_classification.models.train_model import train_epoch

##############################

# constants

##############################

# parameters for the model

class ModelParams:

IN_CHANNELS = "in_channels"

OUT_CHANNELS = "out_channels"

HIDDEN_FEATURES_SIZE = "hidden_features_size"

LAYER_TYPE = "layer_type"

AGGREGATOR = "aggregator"

# parameters for optimizer

class OptimizerParams:

LEARNING_RATE = "learning_rate"

WEIGHT_DECAY = "weight_decay"

# parameters for data

class DataParams:

SPLIT_RATIO = "split_ratio"

METRICS = "metrics"

# parameters relevant to memgraph database

class MemgraphParams:

NODE_ID_PROPERTY = "node_id_property"

# parameters for training

class TrainParams:

NUM_EPOCHS = "num_epochs"

CONSOLE_LOG_FREQ = "console_log_freq"

CHECKPOINT_FREQ = "checkpoint_freq"

BATCH_SIZE = "batch_size"

MAX_MODELS_TO_KEEP = "max_models_to_keep"

TIME_BETWEEN_CHECKPOINTS = "time_between_checkpoints"

# parameters relevant for heterogeneous structure

class HeteroParams:

FEATURES_NAME = "features_name"

OBSERVED_ATTRIBUTE = "observed_attribute"

CLASS_NAME = "class_name"

REINDEXING = "reindexing"

INV_REINDEXING = "inv_reindexing"

NUM_NODES_SAMPLE = "num_nodes_sample"

NUM_ITERATIONS_SAMPLE = "num_iterations_sample"

LABEL_REINDEXING = "label_reindexing"

INV_LABEL_REINDEXING = "inv_label_reindexing"

# other necessary parameters

class OtherParams:

DEVICE_TYPE = "device_type"

PATH_TO_MODEL = "path_to_model"

PATIENCE = "patience"

MODEL_SAVING_FOLDER = "model_saving_folder"

GAT_MODEL = "GAT"

GATV2_MODEL = "GATv2"

SAGE_MODEL = "SAGE"

GAT_WITH_JK = "GATJK"

# dictionary of models

MODELS = {GAT_MODEL: GAT, GATV2_MODEL: GATv2, SAGE_MODEL: SAGE, GAT_WITH_JK: GATJK}

global model, current_values

model: mgp.Any = None

current_values: typing.Dict = {}

# list for saving logged data

logged_data: mgp.List = []

# dictionary of defined input types

DEFINED_INPUT_TYPES = {

ModelParams.HIDDEN_FEATURES_SIZE: list,

ModelParams.LAYER_TYPE: str,

TrainParams.NUM_EPOCHS: int,

OptimizerParams.LEARNING_RATE: float,

OptimizerParams.WEIGHT_DECAY: float,

DataParams.SPLIT_RATIO: float,

MemgraphParams.NODE_ID_PROPERTY: str,

OtherParams.DEVICE_TYPE: str,

TrainParams.CONSOLE_LOG_FREQ: int,

TrainParams.CHECKPOINT_FREQ: int,

TrainParams.BATCH_SIZE: int,

TrainParams.MAX_MODELS_TO_KEEP: int,

TrainParams.TIME_BETWEEN_CHECKPOINTS: float,

ModelParams.AGGREGATOR: str,

DataParams.METRICS: list,

HeteroParams.OBSERVED_ATTRIBUTE: str,

HeteroParams.FEATURES_NAME: str,

HeteroParams.CLASS_NAME: str,

HeteroParams.REINDEXING: dict,

HeteroParams.INV_REINDEXING: dict,

HeteroParams.NUM_NODES_SAMPLE: int,

HeteroParams.NUM_ITERATIONS_SAMPLE: int,

OtherParams.PATH_TO_MODEL: str,

OtherParams.PATIENCE: int,

OtherParams.MODEL_SAVING_FOLDER: str,

}

# dictionary of default values for input types

DEFAULT_VALUES = {

ModelParams.HIDDEN_FEATURES_SIZE: [16, 16],

ModelParams.LAYER_TYPE: "GATJK",

TrainParams.NUM_EPOCHS: 100,

OptimizerParams.LEARNING_RATE: 0.1,

OptimizerParams.WEIGHT_DECAY: 5e-4,

DataParams.SPLIT_RATIO: 0.8,

MemgraphParams.NODE_ID_PROPERTY: "id",

OtherParams.DEVICE_TYPE: "cpu",

TrainParams.CONSOLE_LOG_FREQ: 5,

TrainParams.CHECKPOINT_FREQ: 5,

TrainParams.BATCH_SIZE: 64,

TrainParams.MAX_MODELS_TO_KEEP: 5,

TrainParams.TIME_BETWEEN_CHECKPOINTS: 2.0,

ModelParams.AGGREGATOR: "mean",

DataParams.METRICS: [

"loss",

"accuracy",

"f1_score",

"precision",

"recall",

"num_wrong_examples",

HeteroParams.OBSERVED_ATTRIBUTE: "",

HeteroParams.FEATURES_NAME: "features",

HeteroParams.CLASS_NAME: "class",

HeteroParams.REINDEXING: {},

HeteroParams.INV_REINDEXING: {},

HeteroParams.NUM_NODES_SAMPLE: 512,

HeteroParams.NUM_ITERATIONS_SAMPLE: 4,

OtherParams.PATH_TO_MODEL: "",

OtherParams.PATIENCE: 10,

OtherParams.MODEL_SAVING_FOLDER: "/tmp/torch_models",

}

##############################

# set model parameters

##############################

def declare_data(ctx: mgp.ProcCtx) -> HeteroData:

"""This function initializes global variable data.

Args:

ctx (mgp.ProcCtx): current context

"""

global current_values

# change device type to cuda if possible

current_values[OtherParams.DEVICE_TYPE] = torch.device(

"cuda:0" if torch.cuda.is_available() else "cpu"

)

nodes = list(iter(ctx.graph.vertices)) # obtain nodes from context

if not nodes:

raise Exception("Graph is empty.")

# extraction of data from database to torch.Tensors

(

data,

current_values[HeteroParams.OBSERVED_ATTRIBUTE],

current_values[HeteroParams.REINDEXING],

current_values[HeteroParams.INV_REINDEXING],

current_values[HeteroParams.LABEL_REINDEXING],

current_values[HeteroParams.INV_LABEL_REINDEXING],

) = extract_from_database(

nodes,

current_values[DataParams.SPLIT_RATIO],

current_values[HeteroParams.FEATURES_NAME],

current_values[HeteroParams.CLASS_NAME],

current_values[OtherParams.DEVICE_TYPE],

)

observed_attribute_data = data[current_values[HeteroParams.OBSERVED_ATTRIBUTE]]

# second parameter of shape of feature matrix is number of input channels

current_values[ModelParams.IN_CHANNELS] = observed_attribute_data.x.size(dim=1)

# number of output channels is number of classes in the dataset

current_values[ModelParams.OUT_CHANNELS] = len(

set(observed_attribute_data.y.detach().cpu().numpy())

)

return data

def declare_model(data: mgp.Any):

"""This function initializes global variables model, opt and criterion.

Args:

ctx (mgp.ProcCtx): current context

"""

# choose one of the available layer types

global model, current_values

args_gatjk = [

current_values[ModelParams.IN_CHANNELS],

current_values[ModelParams.HIDDEN_FEATURES_SIZE],

current_values[ModelParams.OUT_CHANNELS],

]

args_inductive = [

current_values[ModelParams.IN_CHANNELS],

current_values[ModelParams.HIDDEN_FEATURES_SIZE],

current_values[ModelParams.OUT_CHANNELS],

current_values[ModelParams.AGGREGATOR],

]

# choose model architecture according to layer type

layer_type = current_values[ModelParams.LAYER_TYPE]

if layer_type not in MODELS.keys():

raise Exception(

"You didn't choose one of currently available models (GAT, GATv2, GATJK and SAGE). Please choose one of them."

)

args = args_gatjk if layer_type == GAT_WITH_JK else args_inductive

model = MODELS[layer_type](*args)

# convert model to hetero structure

# (if graph is homogeneous, we also do this conversion since all calculations are same)

metadata = (data.node_types, data.edge_types)

model = to_hetero(model, metadata)

# move model to device

model.to(current_values[OtherParams.DEVICE_TYPE])

# set default optimizer

opt = torch.optim.Adam(

model.parameters(),

lr=current_values[OptimizerParams.LEARNING_RATE],

weight_decay=current_values[OptimizerParams.WEIGHT_DECAY],

)

# set default criterion

criterion = torch.nn.CrossEntropyLoss()

return opt, criterion

def declare_saving_paths():

"""This function initializes global variables paths."""

global current_values

# either make new folder for saving models, or use existing one with exactly this name

try:

path = os.path.join(

os.getcwd(), current_values[OtherParams.MODEL_SAVING_FOLDER]

)

os.makedirs(path)

print(f"New folder for saving models was created on destination {path}.")

except FileExistsError:

print(f"Folder for saving models already exists on destination {path}.")

current_values[OtherParams.PATH_TO_MODEL] = os.path.join(

os.getcwd(),

current_values[OtherParams.MODEL_SAVING_FOLDER],

"model_" + current_values[ModelParams.LAYER_TYPE] + "_",

)

@mgp.read_proc

def set_model_parameters(

params: mgp.Any = {},

) -> mgp.Record(

hidden_features_size=list,

layer_type=str,

aggregator=str,

learning_rate=float,

weight_decay=float,

split_ratio=float,

metrics=mgp.Any,

node_id_property=str,

num_epochs=int,

console_log_freq=int,

checkpoint_freq=int,

device_type=str,

path_to_model=str,

"""The purpose of this function is to initialize all global variables.

_You_ can change those via **params** dictionary.

It checks if variables in **params** are defined appropriately. If so,

map of default global parameters is overridden with user defined dictionary params.

After that it executes previously defined functions declare_globals and

declare_model_and_data and sets each global variable to some value.

Args:

ctx: (mgp.ProcCtx): current context,

params: (mgp.Map, optional): user defined parameters from query module. Defaults to {}

Raises:

Exception: exception is raised if some variable in dictionary params is not

defined as it should be

Returns:

mgp.Record(

hidden_features_size (list): list of hidden features

layer_type (str): type of layer

aggregator (str): type of aggregator

learning_rate (float): learning rate

weight_decay (float): weight decay

split_ratio (float): ratio between training and validation data

metrics (list): list of metrics to be calculated

node_id_property (str): name of nodes id property

num_epochs (int): number of epochs

console_log_freq (int): frequency of logging metrics

checkpoint_freq (int): frequency of saving models

device_type (str): cpu or cuda

path_to_model (str): path where model is load and saved

)

"""

global DEFINED_INPUT_TYPES, DEFAULT_VALUES, current_values

# function checks if input values in dictionary are correctly typed

def is_correctly_typed(defined_types, input_values):

if isinstance(defined_types, dict) and isinstance(input_values, dict):

# defined_types is a dict of types

return all(

k in input_values # check if exists

and is_correctly_typed(

defined_types[k], input_values[k]

) # check for correct type

for k in defined_types

)

elif isinstance(defined_types, type):

return isinstance(input_values, defined_types)

else:

return False

# hidden_features_size and metrics are sometimes translated as tuples,

# which are not hashable, but conversion to lists makes them hashable

if (

ModelParams.HIDDEN_FEATURES_SIZE in params.keys()

and isinstance(params[ModelParams.HIDDEN_FEATURES_SIZE], tuple)

params[ModelParams.HIDDEN_FEATURES_SIZE] = list(

params[ModelParams.HIDDEN_FEATURES_SIZE]

)

if (

DataParams.METRICS in params.keys()

and isinstance(params[DataParams.METRICS], tuple)

params[DataParams.METRICS] = list(params[DataParams.METRICS])

# override any default parameters

current_values = {**DEFAULT_VALUES, **params}

# raise exception if some variable in dictionary params is not defined as it should be

if not is_correctly_typed(DEFINED_INPUT_TYPES, current_values):

raise Exception("Input dictionary is not correctly typed.")

# define paths

declare_saving_paths()

return mgp.Record(

hidden_features_size=current_values[ModelParams.HIDDEN_FEATURES_SIZE],

layer_type=current_values[ModelParams.LAYER_TYPE],

aggregator=current_values[ModelParams.AGGREGATOR],

learning_rate=current_values[OptimizerParams.LEARNING_RATE],

weight_decay=current_values[OptimizerParams.WEIGHT_DECAY],

split_ratio=current_values[DataParams.SPLIT_RATIO],

metrics=current_values[DataParams.METRICS],

node_id_property=current_values[MemgraphParams.NODE_ID_PROPERTY],

num_epochs=current_values[TrainParams.NUM_EPOCHS],

console_log_freq=current_values[TrainParams.CONSOLE_LOG_FREQ],

checkpoint_freq=current_values[TrainParams.CHECKPOINT_FREQ],

device_type=current_values[OtherParams.DEVICE_TYPE],

path_to_model=current_values[OtherParams.PATH_TO_MODEL],

)

##############################

# train

##############################

def fetch_saved_models():

"""The purpose of this function is to fetch all saved models.

Returns:

model_saving_folder (str): path to folder with saved models

models (list): list of paths of saved models

"""

global model

model_saving_folder = os.path.join(current_values[OtherParams.MODEL_SAVING_FOLDER])

models = [

for f in os.listdir(model_saving_folder)

if os.path.isfile(os.path.join(model_saving_folder, f))

and f.endswith(".pt")

and f.startswith("model")

]

models.sort(reverse=True)

return model_saving_folder, models

def save_model_to_folder() -> str:

"""The purpose of this function is to save model to folder.

Returns:

path_to_saved_model (str): path to saved model

"""

model_saving_folder, models = fetch_saved_models()

# delete oldest models if there are more than max models to keep

for i in range(current_values[TrainParams.MAX_MODELS_TO_KEEP] - 1, len(models)):

os.remove(os.path.join(model_saving_folder, models[i]))

path_to_saved_model = (

current_values[OtherParams.PATH_TO_MODEL]

+ datetime.now().strftime("%Y-%m-%d-%H-%M-%S")

+ ".pt"

)

torch.save(

model.state_dict(),

path_to_saved_model,

)

return path_to_saved_model

@mgp.read_proc

def train(

ctx: mgp.ProcCtx, num_epochs: int = 100

) -> mgp.Record(

epoch=int, loss=float, val_loss=float, train_log=mgp.Any, val_log=mgp.Any

"""This function performs training of model. It first declares data, model,

optimizer and criterion. Then it performs training.

Args:

ctx (mgp.ProcCtx): context of process

num_epochs (int, optional): number of epochs. Defaults to 100.

Raises:

Exception: raised if graph is empty

Returns:

list of mgp.Record of

epoch (int): epoch number

loss (float): loss of model on training data

val_loss (float): loss of model on validation data

train_log (list): list of metrics on training data

val_log (list): list of metrics on validation data

"""

global model, current_values, logged_data

# define fresh data

data = declare_data(ctx)

# define model

opt, criterion = declare_model(data)

current_values[TrainParams.NUM_EPOCHS] = num_epochs

num_nodes_sample = current_values[HeteroParams.NUM_NODES_SAMPLE]

num_iterations_sample = current_values[HeteroParams.NUM_ITERATIONS_SAMPLE]

# variables for early stopping

last_loss = float("inf")

trigger_times = 0

last_time = time()

# training

for epoch in tqdm(range(1, num_epochs + 1)):

# one epoch of training, both training and validation loss are returned

loss, val_loss = train_epoch(

model,

opt,

data,

criterion,

current_values[TrainParams.BATCH_SIZE],

current_values[HeteroParams.OBSERVED_ATTRIBUTE],

{

key: [num_nodes_sample] * num_iterations_sample

for key in data.node_types

)

# early stopping

if val_loss > last_loss:

trigger_times += 1

drop_epochs = (

str(trigger_times)

+ " "

+ ("consecutive epochs" if trigger_times > 1 else "consecutive epoch")

)

times_until_stopping = current_values[OtherParams.PATIENCE] - trigger_times

stop_after = (

str(times_until_stopping)

+ " "

+ ("more drops" if times_until_stopping > 1 else "more drop")

)

print(f"Loss has dropped for {drop_epochs}. Stopping after {stop_after}.")

if trigger_times >= current_values[OtherParams.PATIENCE]:

print("Early stopping!")

break

else:

trigger_times = 0

last_loss = val_loss

# log data every console_log_freq epochs

if epoch % current_values[TrainParams.CONSOLE_LOG_FREQ] == 0:

model.eval()

out = model(data.x_dict, data.edge_index_dict)

dict_train = metrics(

data[current_values[HeteroParams.OBSERVED_ATTRIBUTE]].train_mask,

out,

data,

current_values[DataParams.METRICS],

current_values[HeteroParams.OBSERVED_ATTRIBUTE],

current_values[OtherParams.DEVICE_TYPE],

)

dict_val = metrics(

data[current_values[HeteroParams.OBSERVED_ATTRIBUTE]].val_mask,

out,

data,

current_values[DataParams.METRICS],

current_values[HeteroParams.OBSERVED_ATTRIBUTE],

current_values[OtherParams.DEVICE_TYPE],

)

logged_data.append(

{

"epoch": epoch,

"loss": loss,

"val_loss": val_loss,

"train": dict_train,

"val": dict_val,

}

)

print(

f"Epoch: {epoch:03d}, Loss: {loss:.4f}, Val Loss: {val_loss:.4f},"

+ f'Accuracy: {logged_data[-1]["train"]["accuracy"]:.4f}, Accuracy: {logged_data[-1]["val"]["accuracy"]:.4f}'

)

# save model every checkpoint_freq epochs

if epoch % current_values[TrainParams.CHECKPOINT_FREQ] == 0:

if (

time() - last_time

> current_values[TrainParams.TIME_BETWEEN_CHECKPOINTS]

save_model_to_folder()

last_time = time()

return [

mgp.Record(

epoch=data["epoch"],

loss=data["loss"],

val_loss=data["val_loss"],

train_log=data["train"],

val_log=data["val"],

)

for data in logged_data

]

##############################

# get training data

##############################

@mgp.read_proc

def get_training_data() -> (

mgp.Record(

epoch=int, loss=float, val_loss=float, train_log=mgp.Any, val_log=mgp.Any

)

"""This function is used so user can see what is logged data from training.

Returns:

mgp.Record(

epoch (int): epoch number of record of logged data row

loss (float): loss in logged data row

val_loss (float): validation loss in logged data row

train_log (mgp.Any): training parameters of record of logged data row

val_log (mgp.Any): validation parameters of record of logged data row

): record to return

"""

return [

mgp.Record(

epoch=data["epoch"],

loss=data["loss"],

val_loss=data["val_loss"],

train_log=data["train"],

val_log=data["val"],

)

for data in logged_data

]

##############################

# model loading and saving, predict

##############################

@mgp.read_proc

def save_model() -> mgp.Record(path=str, status=str):

"""This function saves model to model saving folder. If there are already total

of max_models_to_keep models in model saving folder, oldest model is deleted.

Exception: raised if model is not initialized or defined

Returns:

mgp.Record(

path (str): path to saved model

status (str): status of saving model

): return record

"""

if model is None:

raise Exception(

"There are no initialized or loaded models. First load or initialize a model to be able save it."

)

path_to_saved_model = save_model_to_folder()

return mgp.Record(

path=path_to_saved_model, status="Model has been successfully saved."

)

@mgp.read_proc

def load_model(ctx: mgp.ProcCtx, num: int = 0) -> mgp.Record(path=str, status=str):

"""This function loads model from defined folder for saved models.

Args:

num (int, optional): ordinary number of model to load from default map. Defaults to 0 (newest model).

Returns:

mgp.Record(path (str): path to loaded model): return record

"""

global model

data = declare_data(ctx)

declare_model(data)

model_saving_folder, models = fetch_saved_models()

if len(models) == 0:

raise Exception("There are no saved models.")

if len(models) < (len(models) + num) % len(models) + 1:

raise Exception(

f"Model with number {num} does not exist. There are {len(models)} models saved."

)

path_to_load_model = os.path.join(model_saving_folder, models[num])

model.load_state_dict(torch.load(path_to_load_model))

return mgp.Record(

path=path_to_load_model, status="Model has been successfully loaded."

)

@mgp.read_proc

def predict(

ctx: mgp.ProcCtx, vertex: mgp.Vertex

) -> mgp.Record(predicted_class=int, status=str):

"""This function predicts metrics on one node. It is suggested that user previously

loads unseen test data to predict on it.

Example of usage:

MATCH (n {id: 1}) CALL node_classification.predict(n) YIELD * RETURN predicted_class;

# note: if node with property id = 1 doesn't exist, query module won't be called

Args:

ctx (mgp.ProcCtx): proc context

vertex (mgp.Vertex): node to predict on

Returns:

mgp.Record(

predicted_class (int): predicted class

status (str): status of prediction

): record to return

"""

global current_values

# define fresh data

data = declare_data(ctx)

if model is None:

raise Exception("Load a model before predicting.")

model.eval()

out = model(data.x_dict, data.edge_index_dict)

pred = out[current_values[HeteroParams.OBSERVED_ATTRIBUTE]].argmax(dim=1)

inv_reindexing = HeteroParams.INV_REINDEXING

observed_attribute = current_values[HeteroParams.OBSERVED_ATTRIBUTE]

position = current_values[inv_reindexing][observed_attribute][vertex.id]

predicted_class = int(pred.detach().cpu().numpy()[position])

return mgp.Record(

predicted_class=current_values[HeteroParams.INV_LABEL_REINDEXING][

predicted_class

status="Prediction complete.",

)

@mgp.read_proc

def reset() -> mgp.Record(status=str):

"""This function resets all variables to default values.

Returns:

mgp.Record(status (str): status of reset): record to return

"""

# set model and logged_data to None

global model, current_values, logged_data

model = None

logged_data = []

# reinitialize current_values

current_values = DEFAULT_VALUES

return mgp.Record(status="Global parameters and logged data have been reset")

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