X-Git-Url: https://wannabe.guru.org/gitweb/?a=blobdiff_plain;f=ml%2Fmodel_trainer.py;h=e3d89c20421619533da6c8fdcddee739ed33ddff;hb=532df2c5b57c7517dfb3dddd8c1358fbadf8baf3;hp=213a1814cff5e98507e30c19e17669ab123886ce;hpb=36fe954a689c26e7082c61c1c8dbbf76dd7cf6c8;p=python_utils.git diff --git a/ml/model_trainer.py b/ml/model_trainer.py index 213a181..e3d89c2 100644 --- a/ml/model_trainer.py +++ b/ml/model_trainer.py @@ -1,8 +1,10 @@ #!/usr/bin/env python3 -from __future__ import annotations +# © Copyright 2021-2022, Scott Gasch -from abc import ABC, abstractmethod +"""This is a blueprint for training sklearn ML models.""" + +from __future__ import annotations import datetime import glob import logging @@ -10,25 +12,28 @@ import os import pickle import random import sys -from types import SimpleNamespace -from typing import Any, List, NamedTuple, Optional, Set, Tuple import warnings +from abc import ABC, abstractmethod +from dataclasses import dataclass +from types import SimpleNamespace +from typing import Any, List, Optional, Set, Tuple import numpy as np from sklearn.model_selection import train_test_split # type:ignore from sklearn.preprocessing import MinMaxScaler # type: ignore -from ansi import bold, reset import argparse_utils import config -from decorator_utils import timed import executors import parallelize as par +from ansi import bold, reset +from decorator_utils import timed -logger = logging.getLogger(__file__) +logger = logging.getLogger(__name__) parser = config.add_commandline_args( - f"ML Model Trainer ({__file__})", "Arguments related to training an ML model" + f"ML Model Trainer ({__file__})", + "Arguments related to training an ML model", ) parser.add_argument( "--ml_trainer_quiet", @@ -55,6 +60,9 @@ group.add_argument( class InputSpec(SimpleNamespace): + """A collection of info needed to train the model provided by the + caller.""" + file_glob: str feature_count: int features_to_skip: Set[str] @@ -77,15 +85,20 @@ class InputSpec(SimpleNamespace): ) -class OutputSpec(NamedTuple): - model_filename: Optional[str] - model_info_filename: Optional[str] - scaler_filename: Optional[str] - training_score: float - test_score: float +@dataclass +class OutputSpec: + """Info about the results of training returned to the caller.""" + + model_filename: Optional[str] = None + model_info_filename: Optional[str] = None + scaler_filename: Optional[str] = None + training_score: np.float64 = np.float64(0.0) + test_score: np.float64 = np.float64(0.0) class TrainingBlueprint(ABC): + """The blueprint for doing the actual training.""" + def __init__(self): self.y_train = None self.y_test = None @@ -111,13 +124,13 @@ class TrainingBlueprint(ABC): y = np.array(y_) print("Doing random test/train split...") - X_train, X_test, self.y_train, self.y_test = self.test_train_split( + X_train, X_test, self.y_train, self.y_test = TrainingBlueprint.test_train_split( X, y, ) print("Scaling training data...") - scaler, self.X_train_scaled, self.X_test_scaled = self.scale_data( + scaler, self.X_train_scaled, self.X_test_scaled = TrainingBlueprint.scale_data( X_train, X_test, ) @@ -131,16 +144,16 @@ class TrainingBlueprint(ABC): modelid_to_params[model.get_id()] = str(params) best_model = None - best_score = None - best_test_score = None - best_training_score = None + best_score: Optional[np.float64] = None + best_test_score: Optional[np.float64] = None + best_training_score: Optional[np.float64] = None best_params = None for model in smart_future.wait_any(models): params = modelid_to_params[model.get_id()] if isinstance(model, smart_future.SmartFuture): model = model._resolve() if model is not None: - training_score, test_score = self.evaluate_model( + training_score, test_score = TrainingBlueprint.evaluate_model( model, self.X_train_scaled, self.y_train, @@ -170,6 +183,9 @@ class TrainingBlueprint(ABC): print(msg) logger.info(msg) + assert best_training_score is not None + assert best_test_score is not None + assert best_params is not None ( scaler_filename, model_filename, @@ -191,7 +207,7 @@ class TrainingBlueprint(ABC): ) @par.parallelize(method=par.Method.THREAD) - def read_files_from_list(self, files: List[str], n: int) -> Tuple[List, List]: + def read_files_from_list(self, files: List[str]) -> Tuple[List, List]: # All features X = [] @@ -214,21 +230,16 @@ class TrainingBlueprint(ABC): try: (key, value) = line.split(self.spec.key_value_delimiter) except Exception: - logger.debug( - f"WARNING: bad line in file {filename} '{line}', skipped" - ) + logger.debug("WARNING: bad line in file %s '%s', skipped", filename, line) continue key = key.strip() value = value.strip() - if ( - self.spec.features_to_skip is not None - and key in self.spec.features_to_skip - ): - logger.debug(f"Skipping feature {key}") + if self.spec.features_to_skip is not None and key in self.spec.features_to_skip: + logger.debug("Skipping feature %s", key) continue - value = self.normalize_feature(value) + value = TrainingBlueprint.normalize_feature(value) if key == self.spec.label: y.append(value) @@ -275,9 +286,9 @@ class TrainingBlueprint(ABC): results = [] all_files = glob.glob(self.spec.file_glob) self.total_file_count = len(all_files) - for n, files in enumerate(list_utils.shard(all_files, 500)): + for files in list_utils.shard(all_files, 500): file_list = list(files) - results.append(self.read_files_from_list(file_list, n)) + results.append(self.read_files_from_list(file_list)) for result in smart_future.wait_any(results, callback=self.make_progress_graph): result = result._resolve() @@ -289,7 +300,8 @@ class TrainingBlueprint(ABC): print(" " * 80 + "\n") return (X, y) - def normalize_feature(self, value: str) -> Any: + @staticmethod + def normalize_feature(value: str) -> Any: if value in ("False", "None"): ret = 0 elif value == "True": @@ -300,7 +312,8 @@ class TrainingBlueprint(ABC): ret = int(value) return ret - def test_train_split(self, X, y) -> List: + @staticmethod + def test_train_split(X, y) -> List: logger.debug("Performing test/train split") return train_test_split( X, @@ -308,9 +321,8 @@ class TrainingBlueprint(ABC): random_state=random.randrange(0, 1000), ) - def scale_data( - self, X_train: np.ndarray, X_test: np.ndarray - ) -> Tuple[Any, np.ndarray, np.ndarray]: + @staticmethod + def scale_data(X_train: np.ndarray, X_test: np.ndarray) -> Tuple[Any, np.ndarray, np.ndarray]: logger.debug("Scaling data") scaler = MinMaxScaler() scaler.fit(X_train) @@ -318,13 +330,11 @@ class TrainingBlueprint(ABC): # Note: children should implement. Consider using @parallelize. @abstractmethod - def train_model( - self, parameters, X_train_scaled: np.ndarray, y_train: np.ndarray - ) -> Any: + def train_model(self, parameters, X_train_scaled: np.ndarray, y_train: np.ndarray) -> Any: pass + @staticmethod def evaluate_model( - self, model: Any, X_train_scaled: np.ndarray, y_train: np.ndarray, @@ -335,8 +345,9 @@ class TrainingBlueprint(ABC): training_score = model.score(X_train_scaled, y_train) * 100.0 test_score = model.score(X_test_scaled, y_test) * 100.0 logger.info( - f"Model evaluation results: test_score={test_score:.5f}, " - f"train_score={training_score:.5f}" + "Model evaluation results: test_score=%.5f, train_score=%.5f", + test_score, + training_score, ) return (training_score, test_score) @@ -365,18 +376,17 @@ Testing set score: {test_score:.2f}%""" self.spec.persist_percentage_threshold is not None and test_score > self.spec.persist_percentage_threshold ) or ( - not self.spec.quiet - and input_utils.yn_response("Write the model? [y,n]: ") == "y" + not self.spec.quiet and input_utils.yn_response("Write the model? [y,n]: ") == "y" ): scaler_filename = f"{self.spec.basename}_scaler.sav" - with open(scaler_filename, "wb") as f: - pickle.dump(scaler, f) + with open(scaler_filename, "wb") as fb: + pickle.dump(scaler, fb) msg = f"Wrote {scaler_filename}" print(msg) logger.info(msg) model_filename = f"{self.spec.basename}_model.sav" - with open(model_filename, "wb") as f: - pickle.dump(model, f) + with open(model_filename, "wb") as fb: + pickle.dump(model, fb) msg = f"Wrote {model_filename}" print(msg) logger.info(msg)