From 02b17a2203f8176d27ff1cc98fbd0773119dd3c3 Mon Sep 17 00:00:00 2001
From: lucas_miranda <lucasmiranda42@gmail.com>
Date: Wed, 9 Dec 2020 21:01:54 +0100
Subject: [PATCH] Added phenotype classification rule to snakemake pipeline
---
deepof/train_model.py | 62 ++++++++++++++++++++++++++++---------------
deepof/train_utils.py | 47 ++++++++++++++++++++++++++++++++
2 files changed, 87 insertions(+), 22 deletions(-)
diff --git a/deepof/train_model.py b/deepof/train_model.py
index 0b233b97..10b2a382 100644
--- a/deepof/train_model.py
+++ b/deepof/train_model.py
@@ -268,6 +268,8 @@ logparam = {
"k": k,
"loss": loss,
}
+if pheno_class:
+ logparam["pheno_weight"] = pheno_class
# noinspection PyTypeChecker
project_coords = project(
@@ -293,7 +295,7 @@ coords = project_coords.get_coords(
center=animal_id + undercond + "Center",
align=animal_id + undercond + "Spine_1",
align_inplace=True,
- propagate_labels=(pheno_class > 0)
+ propagate_labels=(pheno_class > 0),
)
distances = project_coords.get_distances()
angles = project_coords.get_angles()
@@ -388,17 +390,40 @@ if not tune:
),
]
+ rec = "reconstruction_" if (pheno_class) else ""
+ metrics = [
+ hp.Metric("val_{}mae".format(rec), display_name="val_{}mae".format(rec)),
+ hp.Metric("val_{}mse".format(rec), display_name="val_{}mse".format(rec)),
+ ]
logparam["run"] = run
+ if pheno_class:
+ logparams.append(
+ hp.HParam(
+ "pheno_weight",
+ hp.RealInterval(min_value=0, max_value=1000),
+ display_name="pheno weight",
+ description="weight applied to phenotypic classifier from the latent space",
+ )
+ )
+ metrics.append(
+ [
+ hp.Metric(
+ "phenotype_prediction_accuracy",
+ display_name="phenotype_prediction_accuracy",
+ ),
+ hp.Metric(
+ "phenotype_prediction_auc",
+ display_name="phenotype_prediction_auc",
+ ),
+ ]
+ )
with tf.summary.create_file_writer(
os.path.join(output_path, "hparams", run_ID)
).as_default():
hp.hparams_config(
hparams=logparams,
- metrics=[
- hp.Metric("val_mae", display_name="val_mae"),
- hp.Metric("val_mse", display_name="val_mse"),
- ],
+ metrics=metrics,
)
if not variational:
@@ -512,23 +537,16 @@ if not tune:
)
# Logparams to tensorboard
- def run(run_dir, hpms):
- with tf.summary.create_file_writer(run_dir).as_default():
- hp.hparams(hpms) # record the values used in this trial
- val_mae = tf.reduce_mean(
- tf.keras.metrics.mean_absolute_error(
- X_val, gmvaep.predict(X_val)
- )
- )
- val_mse = tf.reduce_mean(
- tf.keras.metrics.mean_squared_error(
- X_val, gmvaep.predict(X_val)
- )
- )
- tf.summary.scalar("val_mae", val_mae, step=1)
- tf.summary.scalar("val_mse", val_mse, step=1)
-
- run(os.path.join(output_path, "hparams", run_ID), logparam)
+ tensorboard_metric_logging(
+ os.path.join(output_path, "hparams", run_ID),
+ logparam,
+ X_val,
+ y_val,
+ gmvaep,
+ predictor,
+ pheno_class,
+ rec,
+ )
# To avoid stability issues
tf.keras.backend.clear_session()
diff --git a/deepof/train_utils.py b/deepof/train_utils.py
index a8ac8d9d..6f227b1d 100644
--- a/deepof/train_utils.py
+++ b/deepof/train_utils.py
@@ -12,6 +12,7 @@ from datetime import date, datetime
from kerastuner import BayesianOptimization, Hyperband
from kerastuner import HyperParameters
from kerastuner_tensorboard_logger import TensorBoardLogger
+from tensorboard.plugins.hparams import api as hp
from typing import Tuple, Union, Any, List
import deepof.hypermodels
import deepof.model_utils
@@ -136,6 +137,52 @@ def get_callbacks(
return callbacks
+# noinspection PyUnboundLocalVariable
+def tensorboard_metric_logging(
+ run_dir: str,
+ hpms: Any,
+ X: np.ndarray,
+ y: np.ndarray,
+ model: tensorflow.python.keras.engine.functional.Functional,
+ predictor: float,
+ pheno_class: float,
+ rec: str,
+):
+ output = model.predict(X)
+ if pheno_class or predictor:
+ reconstruction = output[0]
+ prediction = output[1]
+ pheno = output[-1]
+ else:
+ reconstruction = output
+
+ with tf.summary.create_file_writer(run_dir).as_default():
+ hp.hparams(hpms) # record the values used in this trial
+ val_mae = tf.reduce_mean(
+ tf.keras.metrics.mean_absolute_error(X, reconstruction)
+ )
+ val_mse = tf.reduce_mean(tf.keras.metrics.mean_squared_error(X, reconstruction))
+ tf.summary.scalar("val_{}mae".format(rec), val_mae, step=1)
+ tf.summary.scalar("val_{}mse".format(rec), val_mse, step=1)
+
+ if predictor:
+ pred_mae = tf.reduce_mean(
+ tf.keras.metrics.mean_absolute_error(X, prediction)
+ )
+ pred_mse = tf.reduce_mean(
+ tf.keras.metrics.mean_squared_error(X, prediction)
+ )
+ tf.summary.scalar("val_prediction_mae".format(rec), pred_mae, step=1)
+ tf.summary.scalar("val_prediction_mse".format(rec), pred_mse, step=1)
+
+ if pheno_class:
+ pheno_acc = tf.keras.metrics.Accuracy(y, pheno)
+ pheno_auc = tf.keras.metrics.AUC(y, pheno)
+
+ tf.summary.scalar("phenotype_prediction_accuracy", pheno_acc, step=1)
+ tf.summary.scalar("phenotype_prediction_auc", pheno_auc, step=1)
+
+
def tune_search(
data: List[np.array],
encoding_size: int,
--
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