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import matplotlib
from sklearn import datasets
import plotly.graph_objects as go
import plotly.express as px
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
matplotlib.use("Agg")
def show_digits():
digits = datasets.load_digits()
fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))
for ax, image, label in zip(axes, digits.images, digits.target):
ax.set_axis_off()
ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest")
ax.set_title("Training: %i" % label)
return fig
def loss_history_plt(loss_history: list[float], loss_fn_name: str):
return px.line(
x=[i for i in range(len(loss_history))],
y=loss_history,
title=f"{loss_fn_name} Loss vs. Training Epoch",
labels={
"x": "Epochs",
"y": f"{loss_fn_name} Loss",
},
)
def hits_and_misses(y_pred: np.ndarray, y_true: np.ndarray):
# decode the one hot encoded predictions
y_pred_decoded = np.argmax(y_pred, axis=1)
y_true_decoded = np.argmax(y_true, axis=1)
hits = y_pred_decoded == y_true_decoded
color = np.where(hits, "Hit", "Miss")
hover_text = [
"True: " + str(y_true_decoded[i]) + ", Pred: " + str(y_pred_decoded[i])
for i in range(len(y_pred_decoded))
]
return px.scatter(
x=np.arange(len(y_pred_decoded)),
y=y_true_decoded,
color=color,
title="Hits and Misses of Predictions",
labels={
"color": "Prediction Correctness",
"x": "Sample Index",
"y": "True Label",
},
color_discrete_map={"Hit": "blue", "Miss": "red"},
hover_name=hover_text,
)
def make_confidence_label(y_pred: np.ndarray, y_test: np.ndarray):
# decode the one hot endoced predictions
y_pred_labels = np.argmax(y_pred, axis=1)
y_test_labels = np.argmax(y_test, axis=1)
confidence_dict: dict[str, float] = {}
for idx, class_name in enumerate([str(i) for i in range(10)]):
class_confidences_idxs = np.where(y_test_labels == idx)[0]
class_confidences = y_pred[class_confidences_idxs, idx]
confidence_dict[class_name] = float(np.mean(class_confidences))
return confidence_dict
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