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class PreTrainedImageClassifier(nn.Module):
    """
    Transfer learning using Resnet models for map image classification.
    """
    def __init__(self, pt_model, dropout=0):
        super().__init__()
        # Set requires_grad = False for pretrained model.
        for param in pt_model.parameters():
            param.requires_grad = False
        

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"""
Script to use for creating static map images in QGIS. 
A dictionary of bounding boxes is required, with key representing the 
linkId and value a list of four coordinates representing the region that 
encloses the link. The dictionary for Charlotte has been included in the 
repository.

Author: James D. Triveri
Date: 2024-02
"""

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import matplotlib.pyplot as plt
from sklearn.metrics import (
    PrecisionRecallDisplay, precision_recall_curve, RocCurveDisplay, roc_curve,
    ConfusionMatrixDisplay, confusion_matrix
    )

beta = 2
prior_thresh = .50

p1, r1, thresh1 = precision_recall_curve(yactual, ypred1)

jtrive84

import matplotlib.pyplot as plt
from sklearn.metrics import (
    PrecisionRecallDisplay, precision_recall_curve, RocCurveDisplay, roc_curve
    )

p1, r1, thresh1 = precision_recall_curve(yactual, ypred1)
p2, r2, thresh2 = precision_recall_curve(yactual, ypred2)

# Determine threshold that maximizes f1 score.