feat(src): implement inference function with damage probability calculations and visualization
Closes #103
This commit is contained in:
nuluh
@@ -1,16 +1,190 @@
|
||||
from src.ml.model_selection import inference_model
|
||||
from joblib import load
|
||||
import pandas as pd
|
||||
from src.data_preprocessing import *
|
||||
from src.process_stft import compute_stft
|
||||
from typing import List, Tuple
|
||||
from sklearn.base import BaseEstimator
|
||||
import json
|
||||
|
||||
x = 30
|
||||
file = f"D:/thesis/data/dataset_B/zzzBD{x}.TXT"
|
||||
sensor = 1
|
||||
model = {"SVM": f"D:/thesis/models/sensor{sensor}/SVM.joblib",
|
||||
"SVM with PCA": f"D:/thesis/models/sensor{sensor}/SVM with StandardScaler and PCA.joblib",
|
||||
"XGBoost": f"D:/thesis/models/sensor{sensor}/XGBoost.joblib"}
|
||||
def probability_damage(pred: Tuple[np.ndarray, np.ndarray], model_classes: BaseEstimator, percentage=False) -> Dict[str, int]:
|
||||
"""
|
||||
Process the prediction output to return unique labels and their counts.
|
||||
"""
|
||||
labels, counts = np.unique(pred, return_counts=True)
|
||||
label_counts = dict(zip(labels, counts))
|
||||
|
||||
index = ((x-1) % 5) + 1
|
||||
inference_model(model["SVM"], file, column_question=index)
|
||||
print("---")
|
||||
inference_model(model["SVM with PCA"], file, column_question=index)
|
||||
print("---")
|
||||
inference_model(model["XGBoost"], file, column_question=index)
|
||||
# init all models classes probability of damage with 0 in dictionary
|
||||
pod: Dict[np.ndarray, int] = dict.fromkeys(model_classes.classes_, 0)
|
||||
|
||||
# update corresponding data
|
||||
pod.update(label_counts)
|
||||
|
||||
# turn the value into ratio instead of prediction counts
|
||||
for label, count in pod.items():
|
||||
|
||||
ratio: float = count/np.sum(counts)
|
||||
|
||||
if percentage:
|
||||
pod[label] = ratio * 100
|
||||
else:
|
||||
pod[label] = ratio
|
||||
return pod
|
||||
|
||||
def convert_keys_to_strings(obj):
|
||||
"""
|
||||
Recursively convert all dictionary keys to strings.
|
||||
"""
|
||||
if isinstance(obj, dict):
|
||||
return {str(key): convert_keys_to_strings(value) for key, value in obj["data"].items()}
|
||||
elif isinstance(obj, list):
|
||||
return [convert_keys_to_strings(item) for item in obj["data"]]
|
||||
else:
|
||||
return obj
|
||||
|
||||
def inference(model_sensor_A_path: str, model_sensor_B_path: str, file_path: str):
|
||||
|
||||
# Generate column indices
|
||||
column_index: List[Tuple[int, int]] = [
|
||||
(i + 1, i + 26)
|
||||
for i in range(5)
|
||||
]
|
||||
# Load a single case data
|
||||
df: pd.DataFrame = pd.read_csv(file_path, delim_whitespace=True, skiprows=10, header=0, memory_map=True)
|
||||
# Take case name
|
||||
case_name: str = file_path.split("/")[-1].split(".")[0]
|
||||
# Extract relevant columns for each sensor
|
||||
column_data: List[Tuple[pd.Series[float], pd.Series[float]]] = [
|
||||
(df.iloc[:, i[0]], df.iloc[:, i[1]])
|
||||
for i in column_index
|
||||
]
|
||||
|
||||
column_data_stft: List[Tuple[pd.DataFrame, pd.DataFrame]] = [
|
||||
(compute_stft(sensor_A), compute_stft(sensor_B))
|
||||
for (sensor_A, sensor_B) in column_data
|
||||
]
|
||||
|
||||
# Load the model
|
||||
model_sensor_A = load(model_sensor_A_path)
|
||||
model_sensor_B = load(model_sensor_B_path)
|
||||
|
||||
res = {}
|
||||
|
||||
for i, (stft_A, stft_B) in enumerate(column_data_stft):
|
||||
# Make predictions using the model
|
||||
pred_A: list[int] = model_sensor_A.predict(stft_A)
|
||||
pred_B: list[int] = model_sensor_B.predict(stft_B)
|
||||
|
||||
|
||||
percentage_A = probability_damage(pred_A, model_sensor_A)
|
||||
percentage_B = probability_damage(pred_B, model_sensor_B)
|
||||
|
||||
|
||||
res[f"Column_{i+1}"] = {
|
||||
"Sensor_A": {
|
||||
# "Predictions": pred_A,
|
||||
"PoD": percentage_A
|
||||
},
|
||||
"Sensor_B": {
|
||||
# "Predictions": pred_B,
|
||||
"PoD": percentage_B
|
||||
}
|
||||
}
|
||||
final_res = {"data": res, "case": case_name}
|
||||
return final_res
|
||||
|
||||
def heatmap(result, damage_classes: list[int] = [1, 2, 3, 4, 5, 6]):
|
||||
from scipy.interpolate import RectBivariateSpline
|
||||
resolution = 300
|
||||
y = list(range(1, len(damage_classes)+1))
|
||||
|
||||
# length of column
|
||||
x = list(range(len(result["data"])))
|
||||
|
||||
# X, Y = np.meshgrid(x, y)
|
||||
Z = []
|
||||
for _, column_data in result["data"].items():
|
||||
sensor_a_pod = column_data['Sensor_A']['PoD']
|
||||
Z.append([sensor_a_pod.get(cls, 0) for cls in damage_classes])
|
||||
Z = np.array(Z).T
|
||||
|
||||
y2 = np.linspace(1, len(damage_classes), resolution)
|
||||
x2 = np.linspace(0,4,resolution)
|
||||
f = RectBivariateSpline(x, y, Z.T, kx=2, ky=2) # 2nd degree quadratic spline interpolation
|
||||
|
||||
Z2 = f(x2, y2).T.clip(0, 1) # clip to ignores negative values from cubic interpolation
|
||||
|
||||
X2, Y2 = np.meshgrid(x2, y2)
|
||||
# breakpoint()
|
||||
c = plt.pcolormesh(X2, Y2, Z2, cmap='jet', shading='auto')
|
||||
|
||||
# Add a colorbar
|
||||
plt.colorbar(c, label='Probability of Damage (PoD)')
|
||||
plt.gca().invert_xaxis()
|
||||
plt.grid(True, linestyle='-', alpha=0.7)
|
||||
plt.xticks(np.arange(int(X2.min()), int(X2.max())+1, 1))
|
||||
plt.xlabel("Column Index")
|
||||
plt.ylabel("Damage Index")
|
||||
plt.title(result["case"])
|
||||
# plt.xticks(ticks=x2, labels=[f'Col_{i+1}' for i in range(len(result))])
|
||||
# plt.gca().xaxis.set_major_locator(MultipleLocator(65/4))
|
||||
plt.show()
|
||||
|
||||
if __name__ == "__main__":
|
||||
import matplotlib.pyplot as plt
|
||||
import json
|
||||
from scipy.interpolate import UnivariateSpline
|
||||
|
||||
|
||||
result = inference(
|
||||
"D:/thesis/models/Sensor A/SVM with StandardScaler and PCA.joblib",
|
||||
"D:/thesis/models/Sensor B/SVM with StandardScaler and PCA.joblib",
|
||||
"D:/thesis/data/dataset_B/zzzBD19.TXT"
|
||||
)
|
||||
|
||||
# heatmap(result)
|
||||
# Convert all keys to strings before dumping to JSON
|
||||
# result_with_string_keys = convert_keys_to_strings(result)
|
||||
# print(json.dumps(result_with_string_keys, indent=4))
|
||||
|
||||
# Create a 5x2 subplot grid (5 rows for each column, 2 columns for sensors)
|
||||
fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(5, 50))
|
||||
|
||||
# # Define damage class labels for x-axis
|
||||
damage_classes = [1, 2, 3, 4, 5, 6]
|
||||
|
||||
# # Loop through each column in the data
|
||||
for row_idx, (column_name, column_data) in enumerate(result['data'].items()):
|
||||
# Plot Sensor A in the first column of subplots
|
||||
sensor_a_pod = column_data['Sensor_A']['PoD']
|
||||
x_values = list(range(len(damage_classes)))
|
||||
y_values = [sensor_a_pod.get(cls, 0) for cls in damage_classes]
|
||||
|
||||
# x2 = np.linspace(1, 6, 100)
|
||||
# interp = UnivariateSpline(x_values, y_values, s=0)
|
||||
axes[row_idx, 0].plot(x_values, y_values, '-', linewidth=2, markersize=8)
|
||||
axes[row_idx, 0].set_title(f"{column_name} - Sensor A", fontsize=10)
|
||||
axes[row_idx, 0].set_xticks(x_values)
|
||||
axes[row_idx, 0].set_xticklabels(damage_classes)
|
||||
axes[row_idx, 0].set_ylim(0, 1.05)
|
||||
axes[row_idx, 0].set_ylabel('Probability')
|
||||
axes[row_idx, 0].set_xlabel('Damage Class')
|
||||
axes[row_idx, 0].grid(True, linestyle='-', alpha=0.5)
|
||||
|
||||
# Plot Sensor B in the second column of subplots
|
||||
sensor_b_pod = column_data['Sensor_B']['PoD']
|
||||
y_values = [sensor_b_pod.get(cls, 0) for cls in damage_classes]
|
||||
axes[row_idx, 1].plot(x_values, y_values, '-', linewidth=2, markersize=8)
|
||||
axes[row_idx, 1].set_title(f"{column_name} - Sensor B", fontsize=10)
|
||||
axes[row_idx, 1].set_xticks(x_values)
|
||||
axes[row_idx, 1].set_xticklabels(damage_classes)
|
||||
axes[row_idx, 1].set_ylim(0, 1.05)
|
||||
axes[row_idx, 1].set_ylabel('Probability')
|
||||
axes[row_idx, 1].set_xlabel('Damage Class')
|
||||
axes[row_idx, 1].grid(True, linestyle='-', alpha=0.5)
|
||||
|
||||
# Adjust layout to prevent overlap
|
||||
fig.tight_layout(rect=[0, 0, 1, 0.96]) # Leave space for suptitle
|
||||
plt.subplots_adjust(hspace=1, wspace=0.3) # Adjust spacing between subplots
|
||||
plt.suptitle(f"Case {result['case']}", fontsize=16, y=0.98) # Adjust suptitle position
|
||||
plt.show()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user