Source code for zamba.models.model

import pickle
from pathlib import Path
from shutil import rmtree
import tempfile

import pandas as pd

    from tensorflow.python import keras
except ImportError:
    msg = "Zamba must have tensorflow installed, run either `pip install zamba[cpu]` "\
          "or `pip install zamba[gpu]` " \
          "depending on what is available on your system."
    raise ImportError(msg)

[docs]class Model(object): """Abstract class implementing required methods for any model in the api. Args: model_path (str | Path) : path to model files Converted to ``Path`` object in ``__init__``. Defaults to ``None``. tempdir (str | Path) : path to temporary diretory Path to temporary directory, if used. Defaults to ``None``. Attributes: model_path (str | Path) : path to model files Converted to ``Path`` object in ``__init__``. Defaults to ``None``. tempdir (str | Path) : path to temporary diretory Path to temporary directory, if used. Defaults to ``None``. delete_tempdir (bool) : whether to clean up tempdir Clean up tempdir if used. """ def __init__(self, model_path=None, tempdir=None, verbose=False): self.model_path = Path(model_path) if model_path is not None else None self.delete_tempdir = tempdir is None self.tempdir = Path(tempfile.mkdtemp()) if self.delete_tempdir else Path(tempdir) self.verbose = verbose def __del__(self): """ If we use the default temporary directory, clean this up when the model is removed. """ if self.delete_tempdir: rmtree(self.tempdir)
[docs] def predict(self, X): """ Args: X: Input to model. Returns: DataFrame of class probabilities. """
[docs] def fit(self): """Use the same architecture, but train the weights from scratch using the provided X and y. Args: X: training inputs Numpy arrays probably y: training labels Class labels Returns: """
[docs] def finetune(self, X, y): """Finetune the network for a different task by keeping the trained weights, replacing the top layer with one that outputs the new classes, and re-training for a few epochs to have the model output the new classes instead. Args: X: y: Returns: """
[docs] def save_model(self): """Save the model weights, checkpoints, to model_path. Returns:
[docs]class SampleModel(Model): """Sample model for testing. Args: model_path: tempdir: """ def __init__(self, model_path=None, tempdir=None, verbose=False): super().__init__(model_path, tempdir=tempdir) self.model = self._build_graph() if self.model_path is None else keras.models.load_model(self.model_path) self.verbose = verbose def _build_graph(self): """Simple keras graph for testing api. Takes two numbers, adds them, also multiplies them, outputs both results. Returns: keras model for testing """ # build simple architecture to multiply two numbers w1 = keras.layers.Input(shape=(1,), name="w1") w2 = keras.layers.Input(shape=(1,), name="w2") add = keras.layers.add([w1, w2]) mult = keras.layers.multiply([w1, w2]) out = keras.layers.concatenate([add, mult]) return keras.models.Model(inputs=[w1, w2], outputs=out)
[docs] def predict(self, X): """ Args: X (list | numpy array) : data for test computation Returns: DataFrame with two columns, ``added`` and ``multiplied``. """ preds = self.model.predict(X) preds = pd.DataFrame(dict(added=preds[:, 0], multiplied=preds[:, 1]))
return preds
[docs] def save_model(self, path=None): """Save the SampleModel. If no path is passed, tries to use model_path attribute. Args: path: Returns: """ # save to user-specified, or model's path path = Path(path) if path else None save_path = path or self.model_path if save_path is None: raise FileNotFoundError("Must provide save_path") # create if necessary save_path.parent.mkdir(exist_ok=True) # keras' save, include_optimizer=False)
[docs] def load_data(self, data_path): """SampleModel loads pickled data Args: data_path: Returns: """ with open(data_path, 'rb') as f: data = pickle.load(f)
return data