# AUTOGENERATED FILE! PLEASE DON'T EDIT
from .callbacks import Callback, Callbacks, Cbs
import k1lib, numpy as np, math
import k1lib.cli as cli
from functools import partial
import matplotlib.pyplot as plt
from typing import Callable
__all__ = ["Loss", "Accuracy"]
def plotF(losses, f): # actual function stored by the sliceable plot
plt.figure(figsize=(10, 3), dpi=100); f = f | cli.deref()
try:
plt.subplot(1, 2, 1); plt.plot(range(len(losses.train)) | f, losses.train | f); plt.title(f"Train loss")
plt.subplot(1, 2, 2); plt.plot(range(len(losses.valid)) | f, losses.valid | f); plt.title(f"Valid loss")
except: pass
def commonPlot(obj, f=cli.iden()):
plotF(obj, f); return
return k1lib.viz.SliceablePlot(partial(plotF, obj, f), docs="""\n\nReminder: the actual slice you put in is for the training plot. The valid loss's plot will update automatically to be in the same time frame""")
def nonEmptyList(_list):
return [0] if _list == [] else _list
[docs]@k1lib.patch(Cbs)
class Loss(Callback):
" "
[docs] def __init__(self, f=lambda l: l.loss):
"""Records losses after each batch.
Expected variables in :class:`~k1lib.Learner`:
- loss: single float value
:param f: optional function to get the loss from :class:`~k1lib.Learner` object"""
super().__init__(); self.order = 20; self.f = f
self.train = []; self.valid = [] # all stats all times
# average stats for each epoch
self.epoch = k1lib.Object.fromDict({"train": [], "valid": []})\
.withRepr("Use...\n" +\
"- `.train` for epoch-averaged training losses\n" +\
"- `.valid` for epoch-averaged validation losses\n" +\
"- `.plot()` to plot the 2 above")
self.plot = partial(commonPlot, self)
self.epoch.plot = partial(commonPlot, self.epoch)
self._trainLosses = []; self._validLosses = []
self._landscape = k1lib.callbacks.Landscape(lambda l: l.loss, "_LossLandscape")
[docs] def endLoss(self):
loss = self.f(self.l)
if self.l.model.training: self._trainLosses.append(loss)
else: self._validLosses.append(loss)
[docs] def endEpoch(self):
self.train.extend(self._trainLosses); self.epoch.train.append(np.mean(nonEmptyList(self._trainLosses)))
self.valid.extend(self._validLosses); self.epoch.valid.append(np.mean(nonEmptyList(self._validLosses)))
self._trainLosses = []; self._validLosses = []
@property
def Landscape(self):
"""Gets loss-landscape-plotting Callback.
Example::
l = k1lib.Learner.sample()
l.cbs.add(Cbs.Loss())
l.Loss.Landscape.plot()"""
self.cbs.add(self._landscape); return self._landscape
[docs] def detach(self): self._landscape.detach(); return super().detach()
[docs] def clear(self):
"""Clears saved data"""
self.train = []; self.epoch.train = []
self.valid = []; self.epoch.valid = []
def __repr__(self):
return f"""{super()._reprHead}, use...
- cb.train: for all training losses over all epochs and batches (#epochs * #batches)
- cb.valid: for all validation losses over all epochs and batches (#epochs * #batches)
- cb.plot(): to plot the 2 above
- cb.clear(): to clear saved data
- cb.epoch: for average losses of each epochs
- cb.Landscape: for loss-landscape-plotting Callback
{super()._reprCan}"""
accFMsg = "You have to specify how to compute the accuracy with the AccF callback first"
[docs]@k1lib.patch(Cbs)
class Accuracy(Callback):
" "
[docs] def __init__(self, variable:str="accuracy"):
"""Records accuracies after each batch.
Expected variables in :class:`~k1lib.Learner`:
- accuracy: single float value from 0 to 1
:param variable: name of variable expected to be available in Learner"""
super().__init__(); self.order = 20
self.train = [0]; self.valid = [0]; self.paused = True; self.variable = variable
self._landscape = k1lib.callbacks.Landscape(lambda l: l.__dict__[variable], "_AccuracyLandscape")
@property
def hasAccF(self):
return any(isinstance(cb, Cbs.AccF) for cb in self.l.cbs.cbs)
[docs] def startRun(self):
self.paused = not self.hasAccF
if not self.paused:
self.train = list(self.train); self.valid = list(self.valid)
[docs] def endRun(self):
if not self.paused:
self.train = np.array(self.train); self.valid = np.array(self.valid)
[docs] def endLoss(self):
if not self.paused:
(self.train if self.l.model.training else self.valid).append(self.l.__dict__[self.variable])
[docs] def plot(self, f=cli.iden()):
"""
:param f:Optional post-processing cli"""
if not self.hasAccF: raise RuntimeError(accFMsg)
plt.figure(figsize=(10, 3), dpi=100); f = f | cli.deref()
try:
plt.subplot(1, 2, 1); plt.plot(range(len(self.train)) | f, 100*self.train | f); plt.title(f"Train accuracy")
plt.subplot(1, 2, 2); plt.plot(range(len(self.valid)) | f, 100*self.valid | f); plt.title(f"Valid accuracy")
except: pass
@property
def Landscape(self):
"""Gets accuracy-landscape-plotting Callback.
Example::
l = k1lib.Learner.sample()
l.add(Cbs.Accuracy())
l.Accuracy.Landscape.plot()
This exact example won't work, as the sample :class:`~k1lib.Learner` task is not
categorical, but the general idea still stands"""
if self.hasAccF:
self._landscape.parent = self
self.cbs.add(self._landscape); return self._landscape
else: raise RuntimeError(f"{accFMsg}, before you can view the landscape")
[docs] def clear(self):
"""Clears saved data."""
self.train = [0]; self.valid = [0]
def __repr__(self):
return f"""{super()._reprHead}{f" (.accuracyF not defined yet)" if not self.hasAccF else ""}, use...
- a.train: for train accuracies over all batches
- a.valid: for train accuracies over all batches
- a.plot(): to plot the 2 above
- a.clear(): to clear saved data
- a.Landscape: for loss-landscape-plotting Callback
{super()._reprCan}"""